Covid Data Indonesia Time Series Data.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dtm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d8f5ac1",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    Province/State      Country/Region        Lat        Long  1/22/20  \\\n",
       "0              NaN         Afghanistan  33.939110   67.709953        0   \n",
       "1              NaN             Albania  41.153300   20.168300        0   \n",
       "2              NaN             Algeria  28.033900    1.659600        0   \n",
       "3              NaN             Andorra  42.506300    1.521800        0   \n",
       "4              NaN              Angola -11.202700   17.873900        0   \n",
       "..             ...                 ...        ...         ...      ...   \n",
       "274            NaN             Vietnam  14.058324  108.277199        0   \n",
       "275            NaN  West Bank and Gaza  31.952200   35.233200        0   \n",
       "276            NaN               Yemen  15.552727   48.516388        0   \n",
       "277            NaN              Zambia -13.133897   27.849332        0   \n",
       "278            NaN            Zimbabwe -19.015438   29.154857        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  8/2/21  8/3/21  8/4/21  \\\n",
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       "..       ...      ...      ...      ...      ...  ...     ...     ...     ...   \n",
       "274        2        2        2        2        2  ...  157507  174461  181756   \n",
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       "\n",
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       "3     14809   14836   14836   14836   14836    14873    14891  \n",
       "4     43269   43487   43592   43662   43747    43890    43998  \n",
       "..      ...     ...     ...     ...     ...      ...      ...  \n",
       "274  189066  193381  205656  215560  224894   232937   241543  \n",
       "275  317534  317703  317703  317999  317999   318181   318435  \n",
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       "277  198455  199135  199662  200049  200201   200830   201342  \n",
       "278  114489  115445  115890  116327  116853   117258   117954  \n",
       "\n",
       "[279 rows x 572 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_case=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n",
    "data_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56a9fae2",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1018</td>\n",
       "      <td>1022</td>\n",
       "      <td>1026</td>\n",
       "      <td>1032</td>\n",
       "      <td>1042</td>\n",
       "      <td>1044</td>\n",
       "      <td>1049</td>\n",
       "      <td>1053</td>\n",
       "      <td>1057</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>14.058324</td>\n",
       "      <td>108.277199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1306</td>\n",
       "      <td>2071</td>\n",
       "      <td>2327</td>\n",
       "      <td>2720</td>\n",
       "      <td>3016</td>\n",
       "      <td>3250</td>\n",
       "      <td>3397</td>\n",
       "      <td>3757</td>\n",
       "      <td>4145</td>\n",
       "      <td>4487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3609</td>\n",
       "      <td>3609</td>\n",
       "      <td>3609</td>\n",
       "      <td>3609</td>\n",
       "      <td>3611</td>\n",
       "      <td>3611</td>\n",
       "      <td>3613</td>\n",
       "      <td>3613</td>\n",
       "      <td>3615</td>\n",
       "      <td>3615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1378</td>\n",
       "      <td>1380</td>\n",
       "      <td>1380</td>\n",
       "      <td>1383</td>\n",
       "      <td>1384</td>\n",
       "      <td>1384</td>\n",
       "      <td>1386</td>\n",
       "      <td>1389</td>\n",
       "      <td>1391</td>\n",
       "      <td>1392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3412</td>\n",
       "      <td>3422</td>\n",
       "      <td>3430</td>\n",
       "      <td>3447</td>\n",
       "      <td>3459</td>\n",
       "      <td>3474</td>\n",
       "      <td>3484</td>\n",
       "      <td>3491</td>\n",
       "      <td>3499</td>\n",
       "      <td>3509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3635</td>\n",
       "      <td>3676</td>\n",
       "      <td>3711</td>\n",
       "      <td>3754</td>\n",
       "      <td>3805</td>\n",
       "      <td>3826</td>\n",
       "      <td>3900</td>\n",
       "      <td>3919</td>\n",
       "      <td>3950</td>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 572 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region        Lat        Long  1/22/20  \\\n",
       "0              NaN         Afghanistan  33.939110   67.709953        0   \n",
       "1              NaN             Albania  41.153300   20.168300        0   \n",
       "2              NaN             Algeria  28.033900    1.659600        0   \n",
       "3              NaN             Andorra  42.506300    1.521800        0   \n",
       "4              NaN              Angola -11.202700   17.873900        0   \n",
       "..             ...                 ...        ...         ...      ...   \n",
       "274            NaN             Vietnam  14.058324  108.277199        0   \n",
       "275            NaN  West Bank and Gaza  31.952200   35.233200        0   \n",
       "276            NaN               Yemen  15.552727   48.516388        0   \n",
       "277            NaN              Zambia -13.133897   27.849332        0   \n",
       "278            NaN            Zimbabwe -19.015438   29.154857        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  8/2/21  8/3/21  8/4/21  \\\n",
       "0          0        0        0        0        0  ...    6774    6804    6836   \n",
       "1          0        0        0        0        0  ...    2457    2457    2457   \n",
       "2          0        0        0        0        0  ...    4329    4370    4404   \n",
       "3          0        0        0        0        0  ...     128     128     128   \n",
       "4          0        0        0        0        0  ...    1018    1022    1026   \n",
       "..       ...      ...      ...      ...      ...  ...     ...     ...     ...   \n",
       "274        0        0        0        0        0  ...    1306    2071    2327   \n",
       "275        0        0        0        0        0  ...    3609    3609    3609   \n",
       "276        0        0        0        0        0  ...    1378    1380    1380   \n",
       "277        0        0        0        0        0  ...    3412    3422    3430   \n",
       "278        0        0        0        0        0  ...    3635    3676    3711   \n",
       "\n",
       "     8/5/21  8/6/21  8/7/21  8/8/21  8/9/21  8/10/21  8/11/21  \n",
       "0      6864    6879    6879    6879    6936     6961     6978  \n",
       "1      2457    2458    2459    2459    2460     2460     2460  \n",
       "2      4441    4487    4520    4550    4578     4618     4654  \n",
       "3       128     128     128     128     128      129      129  \n",
       "4      1032    1042    1044    1049    1053     1057     1063  \n",
       "..      ...     ...     ...     ...     ...      ...      ...  \n",
       "274    2720    3016    3250    3397    3757     4145     4487  \n",
       "275    3609    3611    3611    3613    3613     3615     3615  \n",
       "276    1383    1384    1384    1386    1389     1391     1392  \n",
       "277    3447    3459    3474    3484    3491     3499     3509  \n",
       "278    3754    3805    3826    3900    3919     3950     3991  \n",
       "\n",
       "[279 rows x 572 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_death=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n",
    "data_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18d57d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
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       "      <th>3</th>\n",
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       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
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       "      <th>259</th>\n",
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       "    <tr>\n",
       "      <th>261</th>\n",
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       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>29.154857</td>\n",
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       "</table>\n",
       "<p>264 rows × 572 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region        Lat        Long  1/22/20  \\\n",
       "0              NaN         Afghanistan  33.939110   67.709953        0   \n",
       "1              NaN             Albania  41.153300   20.168300        0   \n",
       "2              NaN             Algeria  28.033900    1.659600        0   \n",
       "3              NaN             Andorra  42.506300    1.521800        0   \n",
       "4              NaN              Angola -11.202700   17.873900        0   \n",
       "..             ...                 ...        ...         ...      ...   \n",
       "259            NaN             Vietnam  14.058324  108.277199        0   \n",
       "260            NaN  West Bank and Gaza  31.952200   35.233200        0   \n",
       "261            NaN               Yemen  15.552727   48.516388        0   \n",
       "262            NaN              Zambia -13.133897   27.849332        0   \n",
       "263            NaN            Zimbabwe -19.015438   29.154857        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  8/2/21  8/3/21  8/4/21  \\\n",
       "0          0        0        0        0        0  ...   82586   82586   82586   \n",
       "1          0        0        0        0        0  ...  130256  130291  130314   \n",
       "2          0        0        0        0        0  ...  116770  117557  118409   \n",
       "3          0        0        0        0        0  ...   14296   14348   14380   \n",
       "4          0        0        0        0        0  ...   38360   39389   39582   \n",
       "..       ...      ...      ...      ...      ...  ...     ...     ...     ...   \n",
       "259        0        0        0        0        0  ...   43157   50831   54332   \n",
       "260        0        0        0        0        0  ...  312149  312289  312320   \n",
       "261        0        0        0        0        0  ...    4212    4232    4251   \n",
       "262        0        0        0        0        0  ...  188950  189341  189658   \n",
       "263        0        0        0        0        0  ...   79420   81570   82994   \n",
       "\n",
       "     8/5/21  8/6/21  8/7/21  8/8/21  8/9/21  8/10/21  8/11/21  \n",
       "0         0       0       0       0       0        0        0  \n",
       "1         0       0       0       0       0        0        0  \n",
       "2         0       0       0       0       0        0        0  \n",
       "3         0       0       0       0       0        0        0  \n",
       "4         0       0       0       0       0        0        0  \n",
       "..      ...     ...     ...     ...     ...      ...      ...  \n",
       "259       0       0       0       0       0        0        0  \n",
       "260       0       0       0       0       0        0        0  \n",
       "261       0       0       0       0       0        0        0  \n",
       "262       0       0       0       0       0        0        0  \n",
       "263       0       0       0       0       0        0        0  \n",
       "\n",
       "[264 rows x 572 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_recover=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n",
    "data_recover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fce77ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def covid_data_merge(df,tipe):\n",
    "    if tipe=='case':\n",
    "        cols=['cases_cum','cases_daily']\n",
    "    elif tipe=='death':\n",
    "        cols=['death_cum','death_daily']\n",
    "    elif tipe=='recover':\n",
    "        cols=['recover_cum','recover_daily']\n",
    "    indo=df[df['Country/Region']=='Indonesia'].drop(['Province/State','Lat','Long'],axis=1)\n",
    "    indo.set_index(indo['Country/Region'],inplace=True)\n",
    "    indo=indo.drop('Country/Region',axis=1).transpose()\n",
    "    indo.columns=[cols[0]]\n",
    "    indo.columns.name='date'\n",
    "    dates=[]\n",
    "    for dt in indo.index:\n",
    "        if len(dt.split('/')[0])==1:\n",
    "            m='0{}'.format(dt.split('/')[0])\n",
    "        else:\n",
    "            m=dt.split('/')[0]\n",
    "\n",
    "        if len(dt.split('/')[1])==1:\n",
    "            d='0{}'.format(dt.split('/')[1])\n",
    "        else:\n",
    "            d=dt.split('/')[1]\n",
    "        dates.append(dtm.datetime.strptime('{}/{}/20{}'.format(m,d,dt.split('/')[2]), '%m/%d/%Y'))\n",
    "    indo.index=dates\n",
    "\n",
    "    daily=indo.iloc[1:].reset_index()-indo.iloc[:-1].reset_index()\n",
    "    indo[cols[1]]=0\n",
    "    indo[cols[1]].iloc[1:]=daily[cols[0]].values\n",
    "    return indo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0230b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "cases=covid_data_merge(data_case,'case')\n",
    "death=covid_data_merge(data_death,'death')\n",
    "recover=covid_data_merge(data_recover,'recover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c1910683",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.concat([cases,death,recover],axis=1)\n",
    "#data=cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a7dc7041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>cases_daily</th>\n",
       "      <th>death_cum</th>\n",
       "      <th>death_daily</th>\n",
       "      <th>recover_cum</th>\n",
       "      <th>recover_daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-07</th>\n",
       "      <td>3639616</td>\n",
       "      <td>31753</td>\n",
       "      <td>105598</td>\n",
       "      <td>1588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-08</th>\n",
       "      <td>3666031</td>\n",
       "      <td>26415</td>\n",
       "      <td>107096</td>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>3686740</td>\n",
       "      <td>20709</td>\n",
       "      <td>108571</td>\n",
       "      <td>1475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-10</th>\n",
       "      <td>3718821</td>\n",
       "      <td>32081</td>\n",
       "      <td>110619</td>\n",
       "      <td>2048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-11</th>\n",
       "      <td>3749446</td>\n",
       "      <td>30625</td>\n",
       "      <td>112198</td>\n",
       "      <td>1579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "date        cases_cum  cases_daily  death_cum  death_daily  recover_cum  \\\n",
       "2020-01-22          0            0          0            0            0   \n",
       "2020-01-23          0            0          0            0            0   \n",
       "2020-01-24          0            0          0            0            0   \n",
       "2020-01-25          0            0          0            0            0   \n",
       "2020-01-26          0            0          0            0            0   \n",
       "...               ...          ...        ...          ...          ...   \n",
       "2021-08-07    3639616        31753     105598         1588            0   \n",
       "2021-08-08    3666031        26415     107096         1498            0   \n",
       "2021-08-09    3686740        20709     108571         1475            0   \n",
       "2021-08-10    3718821        32081     110619         2048            0   \n",
       "2021-08-11    3749446        30625     112198         1579            0   \n",
       "\n",
       "date        recover_daily  \n",
       "2020-01-22              0  \n",
       "2020-01-23              0  \n",
       "2020-01-24              0  \n",
       "2020-01-25              0  \n",
       "2020-01-26              0  \n",
       "...                   ...  \n",
       "2021-08-07              0  \n",
       "2021-08-08              0  \n",
       "2021-08-09              0  \n",
       "2021-08-10              0  \n",
       "2021-08-11              0  \n",
       "\n",
       "[568 rows x 6 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ddacf94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.cases_daily.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eda303d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import pylab\n",
    "pylab.rcParams['figure.figsize'] = (14, 9)\n",
    "#We try to look at the data in yearly period, since the data are in monthly period, we aggregate it into 12 period\n",
    "res = seasonal_decompose(data.cases_daily,model='additive', period=7)\n",
    "ax=res.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d94888c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAADAqPUFJr8jJEnLZpRo9TculCS198cDUDsdIQAAAACTwYk5Qocrz/dKYmgcAAAAgEnSG4goz/EgNPahcQQhAAAAAKPmxByhw2VnuZXv86i1l44QAAAAgAlmrXVk1bijKc/3Ds0VGg2CEAAAAIBRCUZiisas8n1ZTpei8nyf2ugIAQAAAJhoPQNhSUqSjpCPxRIAAAAATLxOfzwIleQ63xEqy/cShAAAAABMvE5/fE5OSa7X4UriHaFOf1iRaGxU1xGEAAAAAIxKVyIIFSdBR6i8IL6EdscoF0wgCAEAAAAYla6hoXFJ0BHKi9fQOsrhcQQhAAAAAKMyOEcomTpC7aPcVJUgBAAAAGBUuvwheT0u5WS5nS5F5fnxIDTaBRMIQgAAAABGpdMfUklulowxTpei8vz40DiCEAAAAIAJ1ekPJ8X8IEnK93nk9bjUxtA4AAAAABOp2x9OivlBkmSMUcUYNlUlCAEAAAAYlU5/SMU5ydERkuLD4+gIAQAAAJhQnf6wSvKSoyMkSWX5PrX10hECAAAAMEGsteryh1ScJHOEpMGOEEEIAAAAwATpC0YUiVmVJMkcISm+hHZHf0ixmB3xNQQhAAAAACPWNbiZalLNEfIpErPqHgiP+BqCEAAAAIARGwpCSdQRKhvDXkIEIQAAAAAj1umPr85Wkpc8HaGKfJ8kqZUgBAAAAGAiDAWhJOoIlRfEg1D7KJbQJggBAAAAGLF3hsYlT0eoPNERYmgcAAAAgAnR0R+SMVJRTvJ0hIpzsuR2GYIQAAAAgInR0htQWZ5XWe7kiRIul1FpnpehcQAAAAAmRnNPUJUF2U6XcYTyfB8dIQAAAAATo6U3oKpCn9NlHKE836tWOkIAAAAAJkJzT1BVhcnXESrN86qznyAEAAAAYJxFojG19QVVWZB8HaGSXK+6/AQhAAAAAOOsrS8ka6XKJOwIFeVkqScQUSQaG9H5BCEAAAAAI9LcE5CkpBwaN7jBa08gMqLzCUIAAAAARqSlN74qWzIuljC4wWvnCIfHEYQAAAAAjEgyd4SKEh2hLn94ROcThAAAAACMSEtPQMZIZXlep0s5QkmiI9Q9QEcIAAAAwDhq7gmqPN8njzv5YkRxTrwj1NlPRwgAAADAOGpO0s1UJal4cGjcAEEIAAAAwDhq6QmqqiD55gdJUmF2loyRulksAQAAAMB4auoeUFVRcgYhl8uoKCdLnSyWAAAAAGC89AUj6vSHNb0k1+lSjqkk18vQOAAAAADjp77DL0maXprjcCXHVpSTpS6GxgEAAAAYL0NBKIk7QsW5WewjBAAAAGD81HcOSJKmlyZvEIoPjaMjBAAAAGCc1Hf4led1qySxTHUyKsrJUhf7CAEAAAAYL/Udfk0vzZUxxulSjqk4N0u9wYjC0dhxzyUIAQAAADiu+k5/Ug+Lk+JD4ySpewQrxxGEAAAAAAzLWqv6joGkXihBineEJI1owQSCEAAAAIBhtfeHNBCOJvXS2ZJUnu+TJDV2DRz3XIIQAAAAgGGlwtLZknTa9GJluY1e3tl23HMJQgAAAACG1dgVkCTVlCR3RyjP59GK2lI9v731uOcShAAAAAAMq6k7PtSsuii5g5AkvWt+hbYe6NWB7sCw5xGEAAAAAAyrqTugnCy3CnM8TpdyXO9aUCFJun9N3bDnEYQAAAAADOtAd0BTi7OTeg+hQQuqCjS9NEe3PbNz2PMIQgAAAACG1dQ9oKlF2U6XMSLGGP3hr87Wb248c9jzkr+3BQAAAMBRTd0BnTWn3OkyRqws36eyxFLax0JHCAAAAMAxRaIxtfQGVV2cGh2hkSIIAQAAADimtr6QojGrKSkyNG6kjhuEjDHZxpjXjDFvGWM2GWP+NXG81BjzpDFmR+JlyUHXfMMYs9MYs80Y856Djp9ujNmQeOw2k5htZYzxGWMeSBxfY4ypnYDnCgAAAGCUGhNLZ6fKHKGRGklHKCjpAmvtqZJOk3SpMeZMSV+X9LS1dp6kpxNvyxizWNI1kpZIulTSj40x7sS9bpd0o6R5iX+XJo7fIKnTWjtX0vclfffEnxoAAACAEzW4H8/UFNhDaDSOG4RsXF/izazEPyvpckl3J47fLemKxOuXS/qNtTZord0jaaeklcaYqZIKrbWrrbVW0j2HXTN4r99KunCwWwQAAADAOU1DQSjzOkIyxriNMesltUh60lq7RlKVtbZJkhIvKxOn10iqP+jy/YljNYnXDz9+yDXW2oikbkllY3g+AAAAAMZRU9eAsrNcKsrJcrqUcTWiIGStjVprT5M0TfHuzknDnH60To4d5vhw1xx6Y2NuNMasNcasbW1tPU7VAAAAAE5UU09AU4tyUmIz1dEY1apx1touSc8pPrenOTHcTYmXLYnT9kuaftBl0yQ1Jo5PO8rxQ64xxngkFUnqOMr7v8Nau9xau7yiomI0pQMAAAAYg/a+oCqOsydPKhrJqnEVxpjixOs5ki6StFXSI5KuS5x2naSHE68/IumaxEpwsxRfFOG1xPC5XmPMmYn5P9ceds3gva6W9ExiHhEAAAAAB7X3hVSa53W6jHHnGcE5UyXdnVj5zSXpQWvto8aY1ZIeNMbcIGmfpA9LkrV2kzHmQUmbJUUkfclaG03c64uSfikpR9LjiX+SdKeke40xOxXvBF0zHk8OAAAAwInp6A9pxawMDELW2rclLT3K8XZJFx7jmlsk3XKU42slHTG/yFobUCJIAQAAAEgO0ZhVpz+ksjTsCI1qjhAAAACAzNHlDylmRRACAAAAkDk6+kOSpNJMXCwBAAAAQGZqTwQhOkIAAAAAMsZQR4ggBAAAACBTtPcFJUll+QQhAAAAABlicGhcSS5BCAAAAECG6OgPqSgnS1nu9IsN6feMAAAAAIyL9r703ENIIggBAAAAOIb2/mBazg+SCEIAAAAAjqGjP5SWK8ZJBCEAAAAAxxAPQum3mapEEAIAAABwFLGYVUc/c4QAAAAAZJCugbBiNj33EJIIQgAAAACOoqM/vpkqc4QAAAAAZIy2vvhmqmXMEQIAAACQKTr640GIjhAAAACAjNGeCELlzBECAAAAkCna++JzhEroCAEAAADIFB39IRVme5TlTs/IkJ7PCgAAAMAJae8PqTw/PRdKkAhCAAAAAI6ioy+UtgslSAQhAAAAAEfR3h8kCAEAAADILB39IZWl6YpxEkEIAAAAwGFiMRsPQmm6mapEEAIAAABwmK6BsGI2fTdTlQhCAAAAAA7T0R/fQ4ihcQAAAAAyRntfSBIdIQAAAAAZpKM/HoSYIwQAAAAgY7QNBiGGxgEAAADIFG298TlCJbkEIQAAAAAZ4kB3QOX5Pnk96RsX0veZAQAAABiTxu4BVRdnO13GhCIIAQAAADhEU3dAU4sIQgAAAAAyhLVWTV0DmlqU43QpE4ogBAAAAGBITyCi/lCUoXEAAAAAMkdT94Ak0RECAAAAkDmaugKSxBwhAAAAAJmjcbAjVExHCAAAAECGONAdkMtIVQU+p0uZUAQhAAAAAEMauwKqLMiWx53eUSG9nx0AAACAUWnqHtDUNF8xTiIIAQAAADhIU3dA1Wm+YpxEEAIAAACQEI1Z7e/0a1opQQgAAABAhmjsGlA4alVblud0KROOIAQAAABAklTX7pckzSzLdbiSiUcQAgAAACBJ2tveL0l0hAAAAABkjrr2fnk9Lk0pZNU4AAAAABmirt2vmaW5crmM06VMOIIQAAAAAEmJIJQBw+IkghAAAAAASbGYVV1Hv2ozYKEEiSAEAAAAQFJLb1CBcEwzy+kIAQAAAMgQ76wYR0cIAAAAQIaoy6ClsyWCEAAAAABJe9v98riMphal/9LZEkEIAAAAgKR97X5NL82Vx50ZESEzniUAAACAYe1t79fMDJkfJBGEAAAAgIxnrVVduz9j5gdJBCEAAAAg47X3h9QXjNARAgAAAJA5Mm3FOIkgBAAAAGS8vW1+SaIjBAAAACBz1LX3y2WkaSUEIQAAAAAZYk+7X9XFOfJ6MiceZM4zBQAAAHBUG/Z3aUl1odNlTCqCEAAAAJDB2vuC2tvu19IZJU6XMqkIQgAAAEAGW1/fJUlaRhACAAAAkCne2Ncpj8vo5Joip0uZVAQhAAAAIIO9UdelRVMLleN1O13KpCIIAQAAABkqGrN6a3+Xls4odrqUSUcQAgAAADJUY9eA/KFoxq0YJxGEAAAAgIzV0DUgKbM2Uh1EEAIAAAAyVENnPAhVF+c4XMnkIwgBAAAAGaox0RGaWpTtcCWTjyAEAAAAZKjG7gGV5/uUnZVZK8ZJBCEAAAAgY+3vHFBNceZ1gySCEAAAAJCxGrsGMnJ+kEQQAgAAADKStVYNXQOqIQgBAAAAyBSd/rAC4RgdIQAAAACZY3DFuJoSghAAAACADLE/sYcQQ+MAAAAAZIzBjhBD4wAAAABkjMauAeVkuVWSm+V0KY4gCAEAAAAZqKFrQNXF2TLGOF2KIwhCAAAAQAbK5D2EJIIQAAAAkJEaugKalqErxkkEIQAAACDjBMJRtfUFVV1EEAIAAACQIZq6A5Iydw8hiSAEAAAAZJxMXzpbGkEQMsZMN8Y8a4zZYozZZIz5SuJ4qTHmSWPMjsTLkoOu+YYxZqcxZpsx5j0HHT/dGLMh8dhtJrFEhTHGZ4x5IHF8jTGmdgKeKwAAAABJDRm+mao0so5QRNLfWmsXSTpT0peMMYslfV3S09baeZKeTrytxGPXSFoi6VJJPzbGuBP3ul3SjZLmJf5dmjh+g6ROa+1cSd+X9N1xeG4AAAAAjqKha0DGSFOKsp0uxTHHDULW2iZr7RuJ13slbZFUI+lySXcnTrtb0hWJ1y+X9BtrbdBau0fSTkkrjTFTJRVaa1dba62kew67ZvBev5V0ocnUBc0BAACACdbQNaCqgmxluTN3psyonnliyNpSSWskVVlrm6R4WJJUmTitRlL9QZftTxyrSbx++PFDrrHWRiR1Syo7yvu/0Riz1hiztrW1dTSlAwAAAEho7BrI6IUSpFEEIWNMvqTfSbrZWtsz3KlHOWaHOT7cNYcesPYOa+1ya+3yioqK45UMAAAA4CgaugY0NYOHxUkjDELGmCzFQ9CvrLX/lzjcnBjupsTLlsTx/ZKmH3T5NEmNiePTjnL8kGuMMR5JRZI6RvtkAAAAAAwvHI2poXNAM8tynS7FUSNZNc5IulPSFmvt9w566BFJ1yVev07SwwcdvyaxEtwsxRdFeC0xfK7XGHNm4p7XHnbN4L2ulvRMYh4RAAAAgHHU0DmgSMyqtizP6VIc5RnBOWdL+pSkDcaY9Ylj35R0q6QHjTE3SNon6cOSZK3dZIx5UNJmxVec+5K1Npq47ouSfikpR9LjiX9SPGjda4zZqXgn6JoTe1oAAAAAjmZve78kqbacIDQsa+1LOvocHkm68BjX3CLplqMcXyvppKMcDygRpAAAAABMnL1tiSCU4R2hzF0vDwAAAMhAe9v9yvO6VZ7vdboURxGEAAAAgAyyt71fteV5yvRtOwlCAAAAQAbZ29af8cPiJIIQAAAAkDHC0Zj2dw6otjyzl86WCEIAAABAxhhcOnsmHSGCEAAAAJApdrf1SZJmZfjS2RJBCAAAAMgYW5p6JUkLphQ4XInzCEIAAABAhtjc2KMZpbkqzM5yuhTHEYQAAACADLGpsVuLpxY6XUZSIAgBAAAAGaAvGNHedr+WVBOEJIIQAAAAkBG2NvVIkhYThCQRhAAAAICMsKkxHoSWVBc5XElyIAgBAAAAGWBzY49K87yqKvQ5XUpSIAgBAAAAGWBTU3yhBGOM06UkBYIQAAAAkObC0Zi2H+hjoYSDEIQAAACANLertU+haIyFEg5CEAIAAADS3KaGwYUSCEKDCEIAAABAmtvc1KPsLJdmlec7XUrSIAgBAAAAaW5zY48WTCmU28VCCYMIQgAAAEAas9ZqU2M3w+IOQxACAAAA0lhD14B6AhEtnkoQOhhBCAAAAEhjmxrjCyWwYtyhCEIAAABAGtvc2COXkRZNIQgdjCAEAAAApLHNTT2aVZ6nHK/b6VKSCkEIAAAASGObG3u0pLrI6TKSDkEIAAAASFNd/pAaugaYH3QUBCEAAAAgTW1uSiyUwIpxRyAIAQAAAGlqMyvGHRNBCAAAAEhT2w70qjzfp/J8n9OlJB2CEAAAAJCmtrf0aX5VvtNlJCWCEAAAAJCGrLXa2dyr+VUFTpeSlAhCAAAAQBpq6BpQfyiqeXSEjoogBAAAAKShHc19kkRH6BgIQgAAAEAa2t7cK0maX0kQOhqCEAAAAJCGtjX3qrLAp6LcLKdLSUoEIQAAACAN7WjuY1jcMAhCAAAAQJqx1mpPW7/mVOQ5XUrSIggBAAAAaabTH1ZfMKIZZQShYyEIAQAAAGmmrr1fkjSzNNfhSpIXQQgAAABIM/s6/JKkGWUEoWMhCAEAAABppj4RhKaXEISOhSAEAAAApJm6dr8qC3zK8bqdLiVpEYQAAACANLOvw68ZzA8aFkEIAAAASDP7OvzMDzoOghAAAACQRgLhqA70BOgIHQdBCAAAAEgjDV0DslYEoeMgCAEAAABpZF97fMW4mQyNGxZBCAAAAEgjg3sITacjNCyCEAAAAJBG6tr9yslyqyLf53QpSY0gBAAAAKSRwaWzjTFOl5LUCEIAAABAGqnv8DMsbgQIQgAAAECasNZqX4efhRJGgCAEAAAApInWvqAGwlGWzh4BghAAAACQJgaXzp5BR+i4CEIAAABAmhhcOpuO0PERhAAAAIA0sa/DL2OkaSU5TpeS9AhCAAAAQJrY1+7XlMJs+Txup0tJegQhAAAAIE3s7xrQ9BKGxY0EQQgAAABIEw2dA6phWNyIEIQAAACANBCNWR3oCai6ONvpUlICQQgAAABIAy29AUVjVjXFDI0bCYIQAAAAkAYaOgckiY7QCBGEAAAAgDTQ0BUPQjXFzBEaCYIQAABIeoFwVC9sb9WDr9crHI05XQ6QlAaDUDVBaEQ8ThcAAAAwnFAkpo//7FW9sa9LktQTCOuz5852tiggCTV2Dag4N0t5Pn7FHwk6QgAAIKnd+vhWvbGvS9+54iSdO69ctz29Q539IafLApJOY1eAYXGjQBACAABJa9uBXt318h5du2qmPnnmTP3j+xerLxjRrY9vlSRZa3XHC7v0rT9uVijCkDlktobOAYbFjQJ9MwAAkLR++cpe+Twu/c1F8yVJC6YU6PPvmqPbn9ul6uIcdfQHdffqOknSxoZu1ZTk6P0nT9VFi6ucLBtwRGPXgFbNKXO6jJRBEAIAAEmp2x/WH95s0BWn1agkzzt0/O8uWaDNjT36/lPbJUmfOnOmFlcX6t/+tEUbGrr14o42vTi3XDlet1OlA5Ouoz+k3mCEoXGjQBACAABJ6Xdv7NdAOKrrzqo95LjbZfSza5drQ0OXqgqzNa0kvnnkNSum6/W9nfrIT1fr3lf36sbz5jhQNeCMNbvbJUlLZxQ7W0gKYY4QAABwzLq6Tn39d2/rEz9/VevqOg957LENTVo0tVCLqwuPuM7rcen0maVDIUiSjDFaOatU584r10+e380y28goL+xoU77Po1OnFztdSsogCAEAAEf85xNbddXtr+ixDU16u75b//rHTbLWSpJaegNat69Tly6ZMur7fuKMGeroD+mt+q5xrhhIXi/tbNWZs8uU5ebX+5HiIwUAACZdfYdfP35uly47tVqrv3Gh/t8HF+vt/d16YlOzJOnJzc2yVrr0pNEHoTNnl8kY6ZVd7eNdNpCU6tr7Vd8xoHPnlTtdSkohCAEAgEn3qzX75DJG33jfQuX5PLpyaY1mV+Tpnx/ZqCc3N+uB1+tVW5ar+VX5o753ca5XS6oL9cqutgmoHEg+L+6I/18/hyA0KgQhAAAwqYKRqB5aW68LF1ZqalF8hSuP26UffmyZvB6XPnfPWm1q7NGN582RMWZM7+OsOeV6o65LgXB0PEsHktJrezpUWeDT7PI8p0tJKawaBwAAJtUj6xvV3h/SJ8+cecjxxdWFevSmc/XklmadNafshDaGXDWnTHe8sFvr6jp19lz+So70tq6uUytqS8f8h4NMRUcIAABMmkg0ph8+u1Mn1RQedT5DUW6Wrj592gmFIElaUVsqj8swPA5pr7FrQA1dAzp9ZonTpaQcghAAAJg0D69vVF27X1++YN6E/vV6cBnhl3eyYALS29rEsvMraksdriT1EIQAAMCkiMasfvjsTi2aWqiLF1dN+Ps7a06Z3t7fpZ5AeMLfF+CUdXs7lOt1a9HUAqdLSTkEIQAAMCFiMatvP7pZv1pTJ0n641uN2tPWr69cOHdS5jKsmlOmmJVe39Mx4e8LcMrrezu1dEaxPOwfNGoslgAAACbEz17crTtf2iNJ2tXSr6e2NGvhlAJdsnj0ewONxbIZJfJ6XHplV7suXDTxHShgsvUFI9p6oEc3XTDP6VJSEtERAACMux3NvfrPJ7bp0iVTdMniKt318h71BML6pw8slss1OStbZWe5tXxmiZ7Z2qJYzE7K+wQm05v7OhWz0opaFkoYCzpCAABg3P154wFFrdW3rzhJ5fleNfcEVVngm7QQNOjDy6fpbx54S89tb9EFC+kKIb28vrdTLiMtnUEQGgs6QgAAYNy9uKNNJ1UXqaLAJ2OMphRlT3oIkqQPnFKt6qJs/fT53ZP+voGJtq6uQ4umFirfR29jLAhCAABgXPUGwnpjX+dR9wmabFlulz5zziyt2dOh1/eyaALSRyQa05v7urSc/YPGjCAEAADG1au7OxSJWZ07r8LpUiRJHz9jhqoKffr2o5uZK4S0sbmpR/5QVMvZP2jMjhuEjDF3GWNajDEbDzpWaox50hizI/Gy5KDHvmGM2WmM2WaMec9Bx083xmxIPHabSaybaYzxGWMeSBxfY4ypHefnCAAAJtGLO1qV63Vr2cxip0uRJOV6Pfr7Sxfq7f3d+u4TW9XSG3C6JOCEvbC9VZJ0xmyC0FiNpCP0S0mXHnbs65KettbOk/R04m0ZYxZLukbSksQ1PzbGuBPX3C7pRknzEv8G73mDpE5r7VxJ35f03bE+GQAA4LwXd7TpzNll8nncxz95klxxWo0uWlSlnz6/Wxd/7wW19wWdLgk4Ic9sbdEp04pUWZDtdCkp67hByFr7gqTDB9VeLunuxOt3S7rioOO/sdYGrbV7JO2UtNIYM1VSobV2tbXWSrrnsGsG7/VbSReaydhlDQAAjLv6Dr/2tPUnxfygg7lcRj+/brl+98Wz1BMI6+5X9jpdEjBmHf0hvVnfpfMXVDpdSkob6xyhKmttkyQlXg5+Fmok1R903v7EsZrE64cfP+Qaa21EUreksjHWBQAAHPTijjZJSpr5QYc7fWaJLllcpbtX16kvGHG6HGBMnt/eImulCxYShE7EeC+WcLROjh3m+HDXHHlzY240xqw1xqxtbW0dY4kAAGCivLijVdVF2ZpTked0Kcf0hXfNUfdAWL9/Y//xTwaS0DNbW1We79PJNUVOl5LSxhqEmhPD3ZR42ZI4vl/S9IPOmyapMXF82lGOH3KNMcYjqUhHDsWTJFlr77DWLrfWLq+oSM6/NAEAkKki0Zhe3tmmc+dVKJlHuS+dUaI5FXl6fOMBp0sBRi0Sjen5bS1694IKR/bmSidjDUKPSLou8fp1kh4+6Pg1iZXgZim+KMJrieFzvcaYMxPzf6497JrBe10t6ZnEPCIAAJBC3tjXpZ5AROfNT/4/Vr5nyRSt2dOhLn/I6VKAURn8OmNY3IkbyfLZv5a0WtICY8x+Y8wNkm6VdLExZoekixNvy1q7SdKDkjZL+rOkL1lro4lbfVHSzxVfQGGXpMcTx++UVGaM2Snpq0qsQAcAAFLLk5sPyOt26V0Lkj8IXbJkiqIxq6e3tBz/ZCCJPLO1RR6X0TlJtiBJKvIc7wRr7ceO8dCFxzj/Fkm3HOX4WkknHeV4QNKHj1cHAABIXtZaPbm5WWfOKVO+77i/XjjulJoiTSnM1hObDuiq06cd/wIgSTy7tUUraktVmJ3ldCkpb7wXSwAAABloV2u/9rb7dfHiKqdLGRGXy+iixZV6eWebQpGY0+UAI9LcE9C25l6dvzD5u66pgCAEAABO2JObmyVJFy1KnXkL582rUH8oqnV1nU6XAozIm/vi/1dX1JY6XEl6IAgBAIAT9tiGJp02vVhTi3KcLmXEVs0pk8dl9MKOsW3J0d4XHOeKgOG9ua9LXrdLi6sLnS4lLRCEAADACdnX7teGhm69/+SpTpcyKgXZWTp9Zome3zb6IHTvq3U6/TtP6f41+yagMuDo3tzXpSU1hfJ53E6XkhYIQgAA4IT8aUOTJOm9J09xuJLRO29+hTY39ai5J3DI8XA0pmAketRrtjT16NuPbpbP49L/e3ijXttz1O0PgXEVjsb0dkOXls0ocbqUtEEQAgAAY2at1cPrG3Ta9GJNK8l1upxRe+9JU5TlNvrWo5s1uI1hJBrTtXe+puXfeUrff3K7AuF4IOoPRnTr41t15Y9fVmF2lh7/yrmaWpytWx7b4uRTQIbY2tSrQDimpTOKnS4lbRCEAADAmD21pUVbD/Tq4ytnOF3KmMyuyNfNF83Xn95u0q9fq5e1Vt/981at3t2uRVMK9YOnd+iq21/R95/crku+/4J+8vwuvfekqfr9X52l2RX5+ty5s/VWfZfW13c5/VSQ5t5ILJSwlI7QuCEIAQCAMYnFrP77L9tUW5arDy2rcbqcMfv8ebN19twyffP3G3TR957Xz17co0+eOUMPfmGV7rxuueo7/LrtmR0qL/Dpd19cpe9/9DRNL413vz60bJryfR7ds3qvs08Cae/V3e2qKc5RdVG206WkjeTf8QwAACSdJzc369bHt2hXa79+cM1p8rhT92+rHrdLv7h+pW59fKv+svmAvnPFSbpmxXRJ0oWLqrTmmxcpau1RN4rN93l01bIa/fq1en3zfYtUnu+b7PKRAWIxq9W723XxoioZY5wuJ22k7nctAADgiKc2N+sL961TltulH1xzmi47tdrpkk6Y1+PS//vgYr309xfok2fOPCTY5XjdRw1Bgz61qlahaEwPvF4/GaUiA21u6lGXP6yz5pY5XUpaIQgBAIAR29LUoy/d/4ZOqi7Ub794li4/rSbj/0I9tzJf58wt132v1ikSjTldDtLQyzvbJElnzSl3uJL0QhACAAAjEghH9eVfv6nCnCzdef2KYbskmebaVTPV1B3QU1uanS4FaejlXe2aW5mvqkLmB40nghAAABiRHzy9Qzta+vS9j5zKXJjDXLioSlWFPj20dr/TpSDNdA+E9erudp07j27QeCMIAQCA42rsGtBdL+3Rh5bW6Nx5FU6Xk3TcLqMrl07Tc9tb1dobdLocpJE/b2xSKBLT5ael7sqMyYogBAAAjut/ntoua6W/uXi+06UkratPr1E0Ft9gFhgvf3izUbPK83TqtCKnS0k7BCEAADCs5p6A/u+NBn38jBlD++fgSHMrC3Ta9GLd92qdgpGo0+UgDdR3+PXqnnZdflp1xi9KMhEIQgAAYFi/WrNPUWv16bNrnS4l6X314vna2+7Xj5/d5XQpSHE9gbBuvHedcrLcumrZNKfLSUss9wIAAI4pFInp/jX7dP6CSs0sy3O6nKR33vwKXXZqtX783E51+kP63Lmz6aJh1IKRqD5/zzrtaO7VXdev4P/QBCEIAQCAY/rVmjq19QV13Vm1TpeSMv7lsiWKxqweeL1eb+7r0iM3nc2wJoyYtVZ/99DbWr27Xd//6Kk6bz6Lk0wUhsYBAJDh/KGIfrtuv17c0aqeQHjoeGPXgP7riW06b36FzmPp3hErzfPqR59Ypu9ccZI2NHTrL5vZWwgjt7GhR398q1E3XzRPVy5lSNxEoiMEAEAGi8asvvzr9UMbgRojLagq0LnzyvXEpmZFrdUtV5xER2MMrlxao9uf26Xv/WW7Ll5UJZeLjyGO7/GNTXK7jK5bVet0KWmPjhAAAGmsNxBWQ9eABkJHrmIWicb0j3/YqKe2NOsf3rdI996wUl+9aL4Ks7P085f2qDDHozuvY37CWHncLn3lonna1tyrP21ocrocpABrrR7feECrZpepJM/rdDlpj44QAABpyFqr+1/bp399ZLNC0ZiKcrL0lQvn6VOrZirL7VJLb0BffeAtvbSzTV86f44+d95sSdK58yr01xfOkz8UUU6Wm07QCfrgKdX60bM79f2ntuu9J02Rx83foHFs25p7taetXzecM8vpUjICQQgAgDT0vSe363+f2alz55XrA6dM1aNvN+lbj27Wfa/WaXltiZ7a0qL+YET/cdUp+siK6Udcn+vlV4Tx4HIZffXi+frCfW/ontV1+gy/4GIYd7+yVy4jXbKkyulSMgLf5QAASDMPvL5P//vMTn10+XT924dOlttl9JHl0/Xsthb9x5+36bltrVpSXaj/94HFmldV4HS5ae89S6bogoWVuuWxLVowpUBnz2XhCRzppR1t+vVr9brxvNmqLMh2upyMYKy1TtcwJsuXL7dr1651ugwAAJJCXzCipq4BvbCjTd/502adM7dcd12/QlkMxUoKvYGwPvTjV9Q1ENZzf/du5fn4WzTeEYnGdOH3npfbGD32lXOVneV2uqS0YYxZZ61dfrTH+CoEACCFHegO6D+e2KrHNjQpEI5Jki5aVKUffnwpISiJFGRn6darTtFVt7+in724WzdfNN/pkpBEntjUrLp2v27/xDJC0CQiCAEAkKKe3dqiL//6TYVjMV21bJpWzipVdpZbFy6sZFJ+Ejp9Zoned/IU3fHCbn3w1GrNqch3uiQkAWut7nhxt2aW5eqSJVOcLiejEIQAAEhBb9V36Yu/Wqe5lfn60ceXaWZZntMlYQS+fukirdndoQ//ZLU+sny6uvwh9QUj+sw5s7RsRonT5cEBa+s69VZ9l759+RK52WtqUhGEAABIMZsau/XpX76u8nyffnH9SlUU+JwuCSM0oyxXv/3iWbrh7tf1sxd3qyzPq1A0pue2terq06fp1d3t+sE1S7VgCotYZIo7XtitktwsXX36kas3YmIRhAAASCL9wYh+/NxO/eHNRt3+yWU6ZVrxIY9vbOjWx3/2qvJ9Ht17wxmEoBQ0qzxPT3/1XbI2vrx2U/eAPvLT1bp79V5le9z624fW6/d/dTZzvDLArtY+PbWlWX99wTzleJkbNNn4CgMAIEnsaevXB3/4kn707C51+UP66oNvKRCODj2+vblX1931mgqys/TgF1ZpVjnD4VKVMUauxDCoqUU5euRL5+iFr52v733kVG1s6NEdL+x2uEJMtFjM6r//sk1ZbpeuXTXT6XIyEh0hAAAcEI3ZQ+YDrK/v0nV3vSa3y+j+z52hcNTqurte0xU/ellLqovU0hvQyzvbVJLr1b03rNS0klwHq8d4K8nzqiTPq+mlubp0yRT98Jmd+tCyGk0tynG6NIyzNbvb9fjGA+roD+mxDQf0tfcsUHk+nV0nEIQAAJhELT0B3fTrN/Xang4V+DwqzMnS1KJsbT3Qq9I8r+674QzNKIuHnO9ccZIeWd+oV3a1qTA7S59/1xxdt6pWU4rYbDGd/cP7F+mZbS269fGt+sE1S50uB+PgsQ1N+qc/bNS8qny9tqdDLmMUiVldf1at/urdc5wuL2OxoSoAAJOguSeg25/bpUfeatRAKKprV81UKBpTlz+sxq4BeT0u/efVpxJyIEn63l+26bZndurLF8zV31w8XxsbenTfq3WaUZarWeV5Qxuynj6zRPkHbc5qrdX6+i49u7VFM8rydPGiKhXlZjn1NDKCtVaN3QG19wVVkuvVtJIcGfNOt/fPGw/opvvf0OyKPEVjVqdOL9a3Lj9J1loVZPO5mWhsqAoAgIPa+4L62B2van/XgN49v0JfvWS+Fk4pdLosJLGvXDRfLb1B3fbMTj24dr/a+oLKcrs0cNCcMUkqysnSp8+u1RWn1ejh9Y363Rv7ta/DP/R4bVmu/nzzeWzSOUHqO/y67q7XtLutf+hYbVmuFkwpUGmeT3Mq8nTr41t1yrQi3XPDGYeEVjiPjhAAABPsmjtW6819Xbrvs2doRW2p0+UgRVhr9bs3GvT89laV5Gbpby9eIElq6hlQXyCi/lBU966u01NbmoeuOWduuS47rVrvWTxFr+5p1+fvXacvXzhPX714vlNP4wj+UETBcEwleV6nSzkhXf6QPnT7K2rvC+mrF89XdXGOmroH9MzWFh3oDmh/54D6ghGdOr1Y996wUoV0fxxBRwgAAIdsaerRq7s79I/vX0QIwqgYY3T16dN09enTDjl+8FC3d82v0JamHj2ztUWXLK7SvKp39h96z5IpuuK0av3kuV26cmlNUqwy+Pb+Ll1712vq8oc1tzJf//XhU3Xa9GKnyxq1YCSqz9+7Tvs7BnTvDSt1xuyyoceuXVUrSQqEo1q9q13La0sYApekWD4bAIAJ9Lt1+5XlNvrQsmnHPxkYg0VTC/Wl8+ceEoIGffP9i+TzuPTPj2yS06OA3qrv0id+tkb5Po/+/tKFGghF9eGfvKI/b2w64tw1u9v18Z+9qst++JL++eGNhywjP5zJeI7WWn39dxu0Zk+H/usjpx4Sgg6WneXW+QsrCUFJjI4QAAATJByN6Q/rG3ThwiqVpvgwIKSmyoJsffWS+frXP27WI2816vLTaoYe84ci2tjQo0A4qiXVhSrL98lae8hEfyn+//iF7a16YXurPnBq9bCdzfoOvx54vV77OvxaNqNYnzxzpjxul7Y39+r6X7ym4rwsPXDjKlUX5+hjK6fr0798XTc/sF5fbuvXpoYeza8q0Pr6Tj27rVU1xTmaXZGnu1fXaX19l+7+zEoV5x7966iha0Df/uNmbWjo1gOfP3NCl5f/1Zp9+v2bDfrbi+frslOrJ+z9YOIxRwgAgHFirVVHf0jFuV6FIjH98yMb9eDa/fr5tct10eIqp8tDhopEY7rqJ6u1qaFb37niJH10xXR969HNuu/VOoWj8d8DC3wenbegQs9ubdE1K2boH96/SN0DYf30+V367br9au8PyRjJbYxuveqUI4brSdKv1tTpX/+4WbGYVXm+Twd6AppdkafFUwv1l03NKszJ0u++uEozy94ZotfaG9TlP3xJjd0BVRT41NobVHm+V9etqtVnz52tHK9bf9l0QDfd/6bOnFOmX1y/4pD9tyRpb1u/rrr9FfWHInIbo7mV+Xrg86uUneXWge6A9rT1a+GUgnGZk7SpsVtX/vgVnTWnTHddt2JoU1wkr+HmCBGEAAAYB5FoTF998C098lajjJEGf7zedP5c/e0l84/4KzswmXoDYX3p/jf1wvZWzavM146WPl19+jS9/+Sp8nlc+ukLu7V2b4dOmVas1bvbVVngU08grFAkpksWT9HVp0/T0hnF+spv1mv17nY99IVVWjajRC29Aa3f16UFUwp08fdf0IraEv3n1adqalG2Ht94QPes3qvtzX06f0GlvvaeBUddHr6pe0CtvUGdXFOknkBEOVlueT2Hzt749Wv79I3/26BLFlfpr86fq5LcLN329E7tbO1Ta09AA+GoHvrCWdrZ0qsv3PeGKgt8Ksv3aUtTjySpqtCnu65foSXVRWP+GPYFI/rg/74kfyiix758rsrYBDUlEIQAAJhA/lBEX33gLf150wFdf1atCrM98npcWjajRGfNLXe6PEBSPKz//KU9+t6T23XtmTP1D+9fdNSA/tDaer24o01l+V594owZmlv5ztyj7oGw3n/bi7JW+rv3zNd/PbFdDV0DKsz2KBKzevpv36WpRTkTUv+Pnt2pHz27U/5QfL6Q1+PSadOK1RMI65YrT9bpM0skSS/uaNXdr+xV90BY71kyRTXFOfrWo5vVG4jot19cNeal6//+t2/roXX1+vXnzjzmvCAkH4IQAAATpKU3oGvvfE3bmnv1D+9bpM+eO9vpkoBhBSNR+Txj31fozX2duuHuteroD6ksz6uPnzFDP31ht/7+0oW64ZxZ41jpkTr7Q3pxZ5vaeoO6YGGlake4El5D14Cu/NHLynK79PBNZ6t8lN2czY09ev//vqjPnjNL//D+xWMpHQ4hCAEAMAFiMatr73pNa+s69NNPLde75lc4XRIwKUKRmNbVdWpORZ4qC7MVisSOGM6WbDbs79ZVt7+iDy2r0a1XnTKqaz915xq9vb9bL3zt/EOWL0fyGy4IJff/WAAAksTR/nB42zM79NLONv3zB5cQgpBRvB6XVs0pU2Vh9tDbye7kaUX6+Bkz9NC6/drX7h/xdevqOvXijjbddP5cQlCaSf7/tQAAOCQcjenbj27Wylue0pJ/fkI/eX6XYjEra63+56nt+p+nduhDS2t0zYrpTpcKYAS++O458riMbntmx4iv+fmLu1WY7dHHz5gxgZXBCewjBADAUbT0BHTzA+v1yq52ve/kKRoIRXXr41v1/LZWLZhSoF++sldXnz5N373qFFaEA1JEVWG2rl01Uz9/aY8+fXbtcVeR29fu1xObDujz75qjPB+/NqcbPqMAkCFaegPa2tQrlzE6fWaJcrxuRaIxtfQGFY1Z7evwy+OKP+Zxj2zAQCxmj7mPRiAclc/jSsmQsGZ3uz5/3zoFwlH914dP1dWnT5O1Vg+t269/eWSTVu9u16fOnKl/vWwJ+4gAKeamC+bpd2806Ft/3Kzf3HjmMb9HRWNW//TwRnlcLl23qnZyi8SkIAgBQJp7Y1+nbn18q17b0zF0zO0yqizwqaM/pGAkdsj5JblZ+siK6brhnFmqLHhnz4/6Dr9ue3qHugbCCoSjau0NamdLnz54arX+5YNLVJjjUUd/SHe+tEe/f7NBTd0B5fs8WlFboi++e65WzjpyN3prrVp7g3K5jMryvJMemg50BxQIRzWlKFvZWfFVtJp7AvqrX72h0lyvfnbdcs2pyJckGWP0keXTdeasMr21v0sfOGVqSoY8INMV5WTpqxfP1z/+YaMeeL1e16w8cshbfzAS7wBvb9W/XXnyUfc/Qupj1TgASGNPbm7WTfe/obI8r65ZOUMrZ5UqEI7qjbpONXQFVJSTpbmV+fK4jKaV5Kh7IKxH3mrUE5sOqCzfp598cplOn1mqN/d16sZ716k/GNGM0lxlZ7lVnJulinyf/u/NBkVjVh6XUSQW/5ly8eIqnVxTpLa+oP70dpPa+0M6c3apvnzhPK1K7L9x35p9uuulPdrT1i9Jml2epy+8e44+fPq0SQkY//bYFt3xwm5JkjHSgqoCLa8t0cs723WgO6BHbjpb86oKjnMXAKkoGrO69q41WlfXqUduOkfzE1/r3QNh3f3KXt318h51+cO6/qxa/ctlSxyuFieC5bMBIAP9eeMBfen+N3RSdaHuun7FqHZB33agV5+7Z632dfhVU5yjhq4BTSnM1j03rBz6hWHQ2/u79ML2VvWHoirJzdI5cyu0uPqdDQsHQlHd/9o+/fT5XWrpDWpJdaEKs7O0ene7ls8s0aUnTZEk/WF9gzY29OjChZX69w+dPLQa1UT409tN+tL9b+jKpTU6a06Z9ncO6NXd7Vpf36Ul1YX66wvm6fyFlRP2/gE4r6U3oPf94EWV5Hr1yE3n6C+bD+jbj25WW19IFy2q1JfOn6ulM0qcLhMniCAEABmksz+kX7y8R7c/v0sn1xTp7s+sVEH26Jd87faH9dC6er26u0PLa0v0sZUzVJQz9qVjA+GoHlxbr0ffblJj14CuWTFdf/XuuUNzbKy1+uUre/Xvj22VyyVdsniKcr1uHegJaMGUgiOG6o1VW19Q5//Xc5pXma/f3LgqJZb9BTAxXtjeqmvvek1TCrN1oCegU6cV6ZYrT9ZJNcMvooDUQRACgDS2rq5DGxt6tL25V89vb9X+zgFJ0mWnVus7V56kwjGEICfVtffrf57aodf2dCgYiak836vtzb3KznLr1qtO0WWnVp/Q/b/20Fv6w/oG/fnm84bm/wDIXN9/crvufGmP/vaS+bp2Va3cLICSVghCAJCG9rb1698f36InNjVLknK9bp0zt1ynzyzR2XPL0+ovmnvb+vV3D72ltXWd+ub7FurG8+aM6T7PbmvRp3/xuj7/rtn6xnsXjXOVAFJVNGYJQGlquCDEqnEAkGLa+oK67ekdun/NPnk9Ln3tPQv00RXTVZLrTdsf5LXlefr1jWfq5t+s178/vlUzy/L0niVTRnWPV3a16Yv3rdOS6kJ9+YJ5E1QpgFSUrt87MTyCEAAkqZaegLYc6JUkFedkaW97v57a0qInNx9QOGp1zYrp+spF88Zl3kwqyHK79N8fOVX7uwZ082/W66EvrBpR16svGNGdL+7Rbc/s0KzyPN3zmZVsjAgAYGgcACSb7oGwvv/kdt2/Zp9C0SP3+HnfyVP16bNnaW5lZs5vaekN6IofvqyYle777ErNrYyvYtflD2lTY4+2NPVoS1OvtjX3yB+KqqUnqL5gRJedWq1brjxpTAtHAABSE3OEACBFvLC9VV/77Vtq6wvpI8un6YrTauR2GXX6w5pSmK3F1YUM4ZC0ubFHn7pzjfyhqK4/u1bhSEz3rK4bCo4VBT4tnFKgopws5fs8+uiK6SyDCwAZiDlCAJBkOvtDenBtvaLWqrooR5UFPv1lc7PuXr1X8yrz9fNrV+jkaemz2MF4W1xdqMe+cq6+8X8bdMcLuxWNWV21bJquWFqtRVMLVT6KPZMAAJmJIAQAk2ggFNVdL+/RT57fpd5A5JDH3C6jj5w+Xf9y2RLleN0OVZg6qgqzddf1K9QbCKsvGNHUohynSwIApBCCEABMgljM6oG19fqfp7aruSeoixZV6mvvWagZpbna1+FXY9eAls4oVnGu1+lSU05BdhbzfgAAo0YQAoBJ8B9PbNNPnt+lZTOK9b8fW6aVs0qHHlswpUALphQ4WB0AAJmHIAQAE8haq7tf2aufPL9LHz9jhm654iQZw2IHAAA4jSAEABPk7f1d+uEzO/WXzc26YGGl/vWyJYQgAACSBEEIAMZRIBzVH99q1H2v1umt/d3K9br19fcu1OfOnc2y1wAAJBGCEACMk+e2tejmB9aryx/WvMp8fevyJbpyaQ0T+QEASEIEIQAYB398q1F/88B6za8q0E8/ebpWziplGBwAAEmMIAQAJ+i+V+v0Tw9v1IraUv38uuUqpAMEAEDSIwgByFg9gbBe2tGmrQd6FY3FdNWyaZpdkT/0uLVWb+3v1iu72tTaG5TbGNWU5OjM2WWKxqz2d/r123X79dSWFl2wsFI//sQyZWexESoAAKmAIAQg7YQiMVlZed2uI4an1bX366ktLXp6S7Ne29OhSMzKZSRjjH707C69a36FLlxUqc7+sB55q0G7WvslSfk+j2LWyh+KHnK/XK9b33zfQn367FnKcrsm7TkCAIATQxACkBJaegN6bU+Hcr1unT6zVEU5hw4/C0dj2tzYoz+sb9D9a/YpGInJZaSC7CydMq1I+T6Ptjf3DgWbeZX5+uy5s3XhokqdOq1YXQMh/XpNvX61pk7Pb2+VJK2sLdXnzp2t9540VUW58fdX3+HXurpOZWe5VVOcozmVecr18q0UAIBUY6y1TtcwJsuXL7dr1651ugwA4yQas6rv8MvrcclljMLRmDr6Q7rr5T16YXurOv3hoXONkRZOKVR1UbbcLqMuf1hvN3QpEI7J7TK64rQaza7I00Aoqvb+kN7c16lIzGpaSU6847OwSjPKco9aRyQaU4c/JJ/HfUTYAgAAqcUYs85au/xoj/FnTABjEoxEte1Ar1p7gxoIR+UPRRUIx4eNGUlv7uvSjpY+dfSHVJKXpU+dOVMfWT5dxhhZaxWMxPRWfZceXLtfu9v6tKO5T33ByBHvJ9fr1gdOmar5VQVaXlsqfyii1/d0at2+TjX3BhSJWuX7PLpmxQwtry3RylmlqizIHvPz8rhdJ3Q9AABIDXSEAIzKQ2vrdc/qOm090KNw9NjfP8rzfVpcXaiyPK92tfbp7f3dmlqUrWAkpt5AeOjawmyPTp5WpNnl+Tq5pkhRa2Wt5HEb+TwunTO3XGX5vsl6egAAII3QEQIwLh5aW6+v/fZtLaku1A3nzNYp04pUU5yjHK9bOVlu5XjdslaKxGKaUpg9tFBBLGb1qzV1en1vpwpzPCrIzlJhdpaqi7N1yeIpyvGy0hoAAJhcBCEAhxgc8rZ6V7ve2Ncpj9ulfK9HdR39enV3h86dV66fX7dcPs/Iw4vLZfSpVbX61KraiSscAABgFAhCANQ9ENav1tTpsQ1N2nagd2jY2uzyPMlI/cGIcrLc+vp7F+q6VbWjCkEAAADJiCAEpDCbmE9jEvvgHI0/FNH+zgE19wS0oaFb+9r9Ks71ysoqEIrqQE9Az21rVTAS04raEn323Nk6uaZIS2cUa2pRziQ/IwAAgMlBEAJSSLc/rK0HerT1QK9e29OhZ7a2aCCxUpvLSHlej/KzPcr3eZTjdau5J6DmnuAh9yjL86onEJbLGGVnuVWQ7dE1K6brIyuma0l1kRNPCwAAYNIRhIAkZa3VhoZuPbW5WRsbe7S1qUeN3YGhxysKfLpiaY2qCn2yNr4PT38oor5ARP2hiPqDUc2vKlBtWa5mlOWpqsCnOZX5KmcFNgAAAIIQkEyiMas39nXq8Q0H9MSmA2roGpDbZTSvMl8rZ5Vq4dRCLZxSoEVTC1VZ4DvmcDgAAAAMjyAEjJP+YESNXQPK8bpVWZAtr8c17Pmd/SH1BiIKRWOqa+/XM1tb9MSmZrX1BeX1uHTevHLdfNE8Xby4SsW53kl6FgAAAJmBIAQMo9sf1mMbm9TcE1B7X0gd/SG19wcVisQUjVlFYlaRqFVbX1Dt/aGh6zwuozkV+Vo4tUALphSoqiBbB3oC2t/p1/7OAe1p69f+zoFD3leu163zF1Tq0pOm6PyFlcr38eUJAAAwUfhNCxnNWqveYETN3QEd6AmoqTugnoH4QgI7Wnr16FtN6g1GJEnFuVkqzfOqNNerPJ9HbpeRx2XkMkbLZpZoemmOaopzNBCKqq7Dr20HerV2b6ceXt849P7K832qKcnRadOL9ckzZ6o836cst9HUohydMq1I2VksSw0AADAZCEJIGtZa9YeiCkViyvW65fO4jpgDY63VQDiqcMQqHIspErUKR2OKxKyisZgC4Zg6/SEZGUViMe1s6VNPIKLsLNdQV2bD/m5taurWge7A0H45R1OY7dF5Cyr0xXfN0YIpBcpyDz/U7Vi6B8Jq7wtqalGOcrwEHQAAgGSQNEHIGHOppB9Ickv6ubX2VodLwihYa9XeH1Jj14AauwbU0BVQU9eAGrvjr7f1BtUfishl4h0Ut0tDr7tcUjQavz4YiQ3d0+MyyvW6leuNLwXdF4yoyx8aNryMxPTSHJ06rVjvOzlH2R638nxuVRVma2pRjqYUZqs4L0uRqFVJbta4LEZQlJOlopysE74PAAAAxk9SBCFjjFvSjyRdLGm/pNeNMY9Yazc7W1lmstYqEI4pFI0pFInJH4qoeyA89K+zP6QDPQEd6A7qQM+AGrsCauwaOCTESFJ2lkvVxTmqLsrRnPI85fk8srKKWSkWs4rGEq9bK5cxKsv3qizPK5/Hpf5QVP7EEtD9wYgGwlHl+zwqzvWqKCdLXo9LWW4jj8slj8vI4zbyuF3yul2HBJj5VfkqyslSfyiqlp6AqgqzlcfcGwAAgIyXLL8RrpS001q7W5KMMb+RdLkkgtAwrLUKRmLyh+JhwZ8ID4NvD4Sj6g++Eyj84Yj8waj6Q/GX/nBU/mBkKHT4Q/G3/eGo7HGaLm6XUWWBT1OKsrWkulAXL65SdVF2PPgUx+fKFI9TR2U85Ps8yq/Id7oMAAAAJIlkCUI1kuoPenu/pDOGu2BnS58u++FLQ7+wW8VfGXr7oF/kB1+1h/12f/i1hx479JqhM4a57ztvD3PfwwLGEfc/yvUH1xSJxhSOWoWiMYWjseMGloP5PK6hoWZ5vvjLXK9bxbnexNuJx7xuZXvd8rpd8nlcyvF6hoZ3FeVkqSQ3S2X5PrldyRFyAAAAgNFKliB0tN+oj/gV3xhzo6QbJalg6myV5XkHjx9yk3eaEOaga3XUc0ziyMGNi8Mf0xHXHnTfY97/0JoOvY85xjUHn3rYOYmXHpcrMSwsPjQsO8utPK9buT6P8hLBJtfrVp7PMxRscn1u5Wa55RnjZH8AAAAg3SRLENovafpBb0+T1Hj4SdbaOyTdIUnLly+3v/j0ysmpDgAAAEBaSZYWweuS5hljZhljvJKukfSIwzUBAAAASFNJ0RGy1kaMMTdJekLx5bPvstZucrgsAAAAAGkqKYKQJFlrH5P0mNN1AAAAAEh/yTI0DgAAAAAmDUEIAAAAQMYhCAEAAADIOAQhAAAAABmHIAQAAAAg4xCEAAAAAGQcghAAAACAjEMQAgAAAJBxCEIAAAAAMg5BCAAAAEDGIQgBAAAAyDgEIQAAAAAZhyAEAAAAIOMQhAAAAABkHIIQAAAAgIxDEAIAAACQcQhCAAAAADKOsdY6XcOYGGN6JR2Q1O10LcdRpOSvcVC5pDanixiBVPqYSqlTb6p8/qXU+ZgOSpV6+T8w/lKlTim1Pv9Scn9sk7m2w6XK5z2VPqZS6tSbKp9/aewf0wXW2oKjPeA5sXoctU3SG9baG50uZDjGmDuSvcZBxpi11trlTtdxPKn0MZVSp95U+fxLqfMxHZQq9fJ/YPylSp1San3+peT+2CZzbYdLlc97Kn1MpdSpN1U+/9LYP6bGmLXHeizVh8b90ekCRiAVakw1qfYxTbV6U0GqfUxTrd5UkCof01SpMxUl88c2mWtLVan2MU21elPBuH9MU3loXMok2FTBxzSz8fkH/wcyG5//zMTnPbNlwud/uOeYyh2hO5wuIA3xMc1sfP7B/4HMxuc/M/F5z2yZ8Pk/5nNM2Y4QAAAAAIxVKneEAAAAAGBMCEIZyBhzpTHGGmMWOl0LnGGM6TvO488ZY9J6zHCmMsZMM8Y8bIzZYYzZZYz5gTHGO8z5NxtjciezRkys4339Iz3xsx/87D8SQSgzfUzSS5KuGc1Fxhj3xJQDYDIYY4yk/5P0B2vtPEnzJeVLumWYy26WRBACUh8/+4HDEIQyjDEmX9LZkm5Q4puhMebdxpgXjDG/N8ZsNsb8xBjjSjzWZ4z5ljFmjaRVzlWO8Zb4vD960Ns/NMZc72BJmHgXSApYa38hSdbaqKS/kfQZY0yeMea/jDEbjDFvG2P+2hjzZUnVkp41xjzrYN0YZ8aYfGPM08aYNxKf88sTx2uNMVuMMT8zxmwyxvzFGJPjdL04MfzsxyB+9h+KIJR5rpD0Z2vtdkkdxphlieMrJf2tpJMlzZH0ocTxPEkbrbVnWGtfmuxiAYyrJZLWHXzAWtsjaZ+kz0qaJWmptfYUSb+y1t4mqVHS+dba8ye7WEyogKQrrbXLJJ0v6b8THUNJmifpR9baJZK6JF3lTIkYR1eIn/3AEQhCmedjkn6TeP03ibcl6TVr7e7EX4h/LemcxPGopN9NbokAJoiRdLSlQo2k8yT9xFobkSRrbcdkFoZJZyT9mzHmbUlPSaqRVJV4bI+1dn3i9XWSaie9Oow3fvYDR+FxugBMHmNMmeJDY04yxlhJbsV/KXpMR/5yNPh2IPENEuknokP/GJLtVCGYNJt02F/3jTGFkqZL2q2jhySkp09IqpB0urU2bIzZq3e+BwQPOi8qiaFxKYyf/TgMP/sPQkcos1wt6R5r7Uxrba21drqkPYr/BWilMWZWYnzwRxWfUIn0VidpsTHGZ4wpknSh0wVhwj0tKdcYc600NAn6vyX9UtJfJH3BGONJPFaauKZXUsHkl4oJViSpJRGCzpc00+mCMGH42Y+D8bP/IAShzPIxSb8/7NjvJH1c0mpJt0raqPg3yMPPQ5pI/KIbtNbWS3pQ0tuSfiXpTUcLw4Sz8R20r5T0YWPMDknbFZ8r8k1JP1d8rtDbxpi3FP++IMV35H6cxRLSw+DXv+Jf88uNMWsV7w5tdbQwTCR+9oOf/cdg4j8XkcmMMe+W9HfW2g84XAomgTHmVEk/s9audLoWAJOLr38M4md/ZuFr/+joCAEZxBjzBcUnxP6j07UAmFx8/QOZia/9Y6MjBAAAACDj0BECAAAAkHEIQgAApCFjzHRjzLPGmC3GmE3GmK8kjpcaY540xuxIvCxJHL/YGLPOGLMh8fKCg+51izGm3hjT59TzAYDxxtA4AADSkDFmqqSp1to3jDEFim+OeoWk6yV1WGtvNcZ8XVKJtfbvjTFLJTVbaxuNMSdJesJaW5O415mKL7u7w1qb78TzAYDxRhACACADGGMelvTDxL93W2ubEmHpOWvtgsPONZLaJFVba4MHHe8jCAFIFwyNAwAgzRljaiUtlbRGUpW1tkmSEi8rj3LJVZLePDgEAUC68ThdAAAAmDjGmHzFN9C82VrbE2/2DHv+EknflXTJJJQHAI6hIwQAQJoyxmQpHoJ+Za39v8Th5sSQuMF5RC0HnT9N0u8lXWut3TXZ9QLAZCIIAQCQhhLzfO6UtMVa+72DHnpE0nWJ16+T9HDi/GJJf5L0DWvty5NYKgA4gsUSAABIQ8aYcyS9KGmDpFji8DcVnyf0oKQZkvZJ+rC1tsMY84+SviFpx0G3ucRa22KM+Q9JH5dULalR0s+ttf8yKU8EACYIQQgAAABAxmFoHAAAAICMQxACAAAAkHEIQgAAAAAyDkEIAAAAQMYhCAEAAADIOAQhAAAAABmHIAQAAAAg4xCEAAAAAGSc/x/jfG+kfgyUHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat=data.cases_daily.rolling(window=10).mean().dropna()\n",
    "dat.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "638f28af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: overflow encountered in exp\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: invalid value encountered in true_divide\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: overflow encountered in exp\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: invalid value encountered in true_divide\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: overflow encountered in exp\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: invalid value encountered in true_divide\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: overflow encountered in exp\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: invalid value encountered in true_divide\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
    "                        FutureWarning)\n",
    "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
    "                        FutureWarning)\n",
    "\n",
    "def find_best_model(data,num_p,num_q):\n",
    "    df=pd.DataFrame() #We make a pandas dataframe to ease our work, you can use any way that makes you comfortable and work easly\n",
    "    pq=[[],[]] #This list for storing the combinations of p and q\n",
    "    aic=[] # This list for storing aic value\n",
    "    for p in range(num_p+1): #plus one is to make the range up to num_p since python range only ranging from 0 to N-1\n",
    "        for q in range(num_q+1):\n",
    "            if p!=0 and q!=0: #this logic will avoid (0,0) value which is not correct\n",
    "                try: #Using exception to avoid the error while training the model with certain p and q value\n",
    "                    model=ARMA(data,order=(p,q))\n",
    "                    result=model.fit()\n",
    "                    pq[0].append(p)\n",
    "                    pq[1].append(q)\n",
    "                    aic.append(result.aic)\n",
    "                except:\n",
    "                    pass #If there is an error caused by the calculation, it will be skipped to the next iteration\n",
    "    df['p']=pq[0]\n",
    "    df['q']=pq[1]\n",
    "    df['AIC']=aic\n",
    "    \n",
    "    df.sort_values('AIC',inplace=True)\n",
    "    \n",
    "    return df.p.iloc[0],df.q.iloc[0],df.AIC.iloc[0],df\n",
    "\n",
    "p,q,AIC,df=find_best_model(dat,5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5b8f9608",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: overflow encountered in exp\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:701: RuntimeWarning: invalid value encountered in true_divide\n",
      "  newparams = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: overflow encountered in exp\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/tsa/tsatools.py:702: RuntimeWarning: invalid value encountered in true_divide\n",
      "  tmp = ((1-np.exp(-params))/(1+np.exp(-params))).copy()\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "/Users/fahmimn21/miniforge3/lib/python3.9/site-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod = ARMA(dat, order=(p,q)) #set MA_order to 1 for MA(1) model, another is 0\n",
    "res = mod.fit()\n",
    "fig,ax=plt.subplots(figsize=(15,5))\n",
    "res.plot_predict(start=0, end=len(data)+60,ax=ax)\n",
    "ax.set_xlim('2021-05-01','2021-09-30')\n",
    "ax.set_ylim(0,48000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233cb6c9",
   "metadata": {},
   "source": [
    "# Forecasting using ML Supervised Model\n",
    "## Univariate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "76a8b80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "834affc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date</th>\n",
       "      <th>cases_cum</th>\n",
       "      <th>cases_daily</th>\n",
       "      <th>death_cum</th>\n",
       "      <th>death_daily</th>\n",
       "      <th>recover_cum</th>\n",
       "      <th>recover_daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-07</th>\n",
       "      <td>3639616</td>\n",
       "      <td>31753</td>\n",
       "      <td>105598</td>\n",
       "      <td>1588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-08</th>\n",
       "      <td>3666031</td>\n",
       "      <td>26415</td>\n",
       "      <td>107096</td>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>3686740</td>\n",
       "      <td>20709</td>\n",
       "      <td>108571</td>\n",
       "      <td>1475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-10</th>\n",
       "      <td>3718821</td>\n",
       "      <td>32081</td>\n",
       "      <td>110619</td>\n",
       "      <td>2048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-11</th>\n",
       "      <td>3749446</td>\n",
       "      <td>30625</td>\n",
       "      <td>112198</td>\n",
       "      <td>1579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "date        cases_cum  cases_daily  death_cum  death_daily  recover_cum  \\\n",
       "2020-01-22          0            0          0            0            0   \n",
       "2020-01-23          0            0          0            0            0   \n",
       "2020-01-24          0            0          0            0            0   \n",
       "2020-01-25          0            0          0            0            0   \n",
       "2020-01-26          0            0          0            0            0   \n",
       "...               ...          ...        ...          ...          ...   \n",
       "2021-08-07    3639616        31753     105598         1588            0   \n",
       "2021-08-08    3666031        26415     107096         1498            0   \n",
       "2021-08-09    3686740        20709     108571         1475            0   \n",
       "2021-08-10    3718821        32081     110619         2048            0   \n",
       "2021-08-11    3749446        30625     112198         1579            0   \n",
       "\n",
       "date        recover_daily  \n",
       "2020-01-22              0  \n",
       "2020-01-23              0  \n",
       "2020-01-24              0  \n",
       "2020-01-25              0  \n",
       "2020-01-26              0  \n",
       "...                   ...  \n",
       "2021-08-07              0  \n",
       "2021-08-08              0  \n",
       "2021-08-09              0  \n",
       "2021-08-10              0  \n",
       "2021-08-11              0  \n",
       "\n",
       "[568 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "35da5ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Forecasting data cased_daily berdasarkan 7 data histori (7 hari)\n",
    "\n",
    "window=7\n",
    "X=[]\n",
    "y=[]\n",
    "for i in range(window,len(data)):\n",
    "    X.append(data.cases_daily.iloc[i-window:i].values)\n",
    "    y.append(data.cases_daily.iloc[i])\n",
    "X=np.array(X)\n",
    "y=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "5f90febe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,     0,     0, ...,     0,     0,     0],\n",
       "       [    0,     0,     0, ...,     0,     0,     0],\n",
       "       [    0,     0,     0, ...,     0,     0,     0],\n",
       "       ...,\n",
       "       [22404, 33900, 35867, ..., 39532, 31753, 26415],\n",
       "       [33900, 35867, 35764, ..., 31753, 26415, 20709],\n",
       "       [35867, 35764, 39532, ..., 26415, 20709, 32081]])"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "2c86e983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>37284</td>\n",
       "      <td>30738</td>\n",
       "      <td>22404</td>\n",
       "      <td>33900</td>\n",
       "      <td>35867</td>\n",
       "      <td>35764</td>\n",
       "      <td>39532</td>\n",
       "      <td>31753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>30738</td>\n",
       "      <td>22404</td>\n",
       "      <td>33900</td>\n",
       "      <td>35867</td>\n",
       "      <td>35764</td>\n",
       "      <td>39532</td>\n",
       "      <td>31753</td>\n",
       "      <td>26415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-02</th>\n",
       "      <td>22404</td>\n",
       "      <td>33900</td>\n",
       "      <td>35867</td>\n",
       "      <td>35764</td>\n",
       "      <td>39532</td>\n",
       "      <td>31753</td>\n",
       "      <td>26415</td>\n",
       "      <td>20709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03</th>\n",
       "      <td>33900</td>\n",
       "      <td>35867</td>\n",
       "      <td>35764</td>\n",
       "      <td>39532</td>\n",
       "      <td>31753</td>\n",
       "      <td>26415</td>\n",
       "      <td>20709</td>\n",
       "      <td>32081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-04</th>\n",
       "      <td>35867</td>\n",
       "      <td>35764</td>\n",
       "      <td>39532</td>\n",
       "      <td>31753</td>\n",
       "      <td>26415</td>\n",
       "      <td>20709</td>\n",
       "      <td>32081</td>\n",
       "      <td>30625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0      1      2      3      4      5      6      y\n",
       "2020-01-22      0      0      0      0      0      0      0      0\n",
       "2020-01-23      0      0      0      0      0      0      0      0\n",
       "2020-01-24      0      0      0      0      0      0      0      0\n",
       "2020-01-25      0      0      0      0      0      0      0      0\n",
       "2020-01-26      0      0      0      0      0      0      0      0\n",
       "...           ...    ...    ...    ...    ...    ...    ...    ...\n",
       "2021-07-31  37284  30738  22404  33900  35867  35764  39532  31753\n",
       "2021-08-01  30738  22404  33900  35867  35764  39532  31753  26415\n",
       "2021-08-02  22404  33900  35867  35764  39532  31753  26415  20709\n",
       "2021-08-03  33900  35867  35764  39532  31753  26415  20709  32081\n",
       "2021-08-04  35867  35764  39532  31753  26415  20709  32081  30625\n",
       "\n",
       "[561 rows x 8 columns]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_forecast=pd.DataFrame(X,index=data.index[:len(data)-window])\n",
    "dat_forecast['y']=y\n",
    "dat_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c2d68602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.39473545, 0.59728315, 0.63193967, ..., 0.69651321, 0.55945522,\n",
       "        0.46540515],\n",
       "       [0.59728315, 0.63193967, 0.63012492, ..., 0.55945522, 0.46540515,\n",
       "        0.36487129],\n",
       "       [0.63193967, 0.63012492, 0.69651321, ..., 0.46540515, 0.36487129,\n",
       "        0.56523424]])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MinMaxScaling\n",
    "\n",
    "minmax=MinMaxScaler()\n",
    "X_scaled=minmax.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "eb775c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size=int(0.7*len(X_scaled))\n",
    "\n",
    "X_train=X_scaled[:train_size]\n",
    "X_test=X_scaled[train_size:]\n",
    "\n",
    "y_train=y[:train_size]\n",
    "y_test=y[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "73f7b4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1267c07f0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "dat_forecast.y.iloc[:train_size].plot(kind='line',ax=ax,label='train')\n",
    "dat_forecast.y.iloc[train_size:].plot(kind='line',ax=ax, label='test')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae297935",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "f06ddb18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3990.257362614317\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model=SVR(kernel='rbf',C=10000,gamma='auto')\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)\n",
    "MAE=mean_absolute_error(y_test,y_pred)\n",
    "print(MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "f2bc2893",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict(X_scaled)\n",
    "dat_forecast['pred']=prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "b130edfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_forecast.pred.plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ab5668",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "560d7086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1943.287647889325\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression(fit_intercept=True)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred=model.predict(X_test)\n",
    "MAE=mean_absolute_error(y_test,y_pred)\n",
    "print(MAE)\n",
    "\n",
    "prediction=model.predict(X_scaled)\n",
    "dat_forecast['pred']=prediction\n",
    "dat_forecast.pred.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "179cb492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-22        0\n",
       "2020-01-23        0\n",
       "2020-01-24        0\n",
       "2020-01-25        0\n",
       "2020-01-26        0\n",
       "              ...  \n",
       "2021-08-07    31753\n",
       "2021-08-08    26415\n",
       "2021-08-09    20709\n",
       "2021-08-10    32081\n",
       "2021-08-11    30625\n",
       "Name: cases_daily, Length: 568, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casedaily=data['cases_daily']\n",
    "casedaily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "7f366cce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x127111d30>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecasting=pd.Series(casedaily.values,index=casedaily.index)\n",
    "days=60\n",
    "for i in range(days):\n",
    "    hist=minmax.transform(forecasting[-window:].values.reshape(1, -1)) #single data\n",
    "    y_for=model.predict(hist)[0]\n",
    "    new_date=forecasting.index[-1]+dt.timedelta(days=1)\n",
    "    forecasting.loc[new_date]=int(y_for)\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "forecasting.plot(kind='line',ax=ax,label='forcasted', color='orange')\n",
    "casedaily.plot(kind='line',ax=ax,label='history',color='blue')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c6afc3",
   "metadata": {},
   "source": [
    "Model exaust dalam forecasting data. Extract feature -> moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ff5b43d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_ma=data.cases_daily.rolling(window=7).mean().dropna()\n",
    "dat_ma.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "b728353b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "def forecasting_process(data,percent_train,window,days):\n",
    "    X=[]\n",
    "    y=[]\n",
    "    for i in range(window,len(data)):\n",
    "        X.append(data.iloc[i-window:i].values)\n",
    "        y.append(data.iloc[i])\n",
    "    X=np.array(X)\n",
    "    y=np.array(y)\n",
    "    \n",
    "    minmax=MinMaxScaler()\n",
    "    X_scaled=minmax.fit_transform(X)\n",
    "    \n",
    "    train_size=int(percent_train*len(X_scaled))\n",
    "\n",
    "    X_train=X_scaled[:train_size]\n",
    "    X_test=X_scaled[train_size:]\n",
    "\n",
    "    y_train=y[:train_size]\n",
    "    y_test=y[train_size:]\n",
    "    \n",
    "    model=LinearRegression(fit_intercept=True)\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred=model.predict(X_test)\n",
    "    MAE=mean_absolute_error(y_test,y_pred)\n",
    "    print('MAE model:',MAE)\n",
    "    \n",
    "    forecasting=pd.Series(data.values,index=data.index)\n",
    "    for i in range(days):\n",
    "        hist=minmax.transform(forecasting[-window:].values.reshape(1, -1)) #single data\n",
    "        y_for=model.predict(hist)[0]\n",
    "        new_date=forecasting.index[-1]+dt.timedelta(days=1)\n",
    "        forecasting.loc[new_date]=int(y_for)\n",
    "    \n",
    "    fig,ax=plt.subplots()\n",
    "    forecasting.plot(kind='line',ax=ax,label='forcasted', color='orange')\n",
    "    data.plot(kind='line',ax=ax,label='history',color='blue')\n",
    "    ax.legend()\n",
    "\n",
    "    return forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "f8bdf925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE model: 232.05432223170374\n"
     ]
    },
    {
     "data": {
      "image/png": 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ELyeV7lAREhERERE5hrpK3/ltiUnd+9XZFduCu/GjsvPsK6nMGLOJ0y+eRGpiA9V1Ub2aU7pHRUhERERE5BjqqhoBSEru3giOK76F+oaPys62ohymTawCIDWpmeq62N4LKd2mIiQiIiIicgx11b77ByUlR3ZrO1dcG+4m341Vq0qqqWlMYfQo33TaqUmtVNcn9G5Q6RYVIRERERGRY6irbgEgKTW6W9u5EtpxN/lGfXZsKAFg9Em+G62mpnRQ7U7sxZTSXSpCIiIiIiLHUFfTBkBSavdOZUtM6DhYhAo21wEwakIaAKkplprGZLyeI2+4Kv1DRUhERERE5Bjqan03Pk1Ki+/WdokuL7WNiXg9Xgq2tRNmPIyYmANAaqrB2jDqKut7Pa90jYqQiIiIiMgx1Nf5Rm0SU7tXhE4aF0ZzWxyFG4so2BnJsIwSouN8p9elpvmm1a7eX9e7YaXLVIRERERERI7B7Zs9m4Tk7k1uMO30DABWLd5HwZ4URg8tP/hcaoZvNrnq8sbeCSndpiIkIiIiInIMbjfERTcSHhl+/JUPMeHUEUSGt7FqZRsF+4YwKvej0pOa7rt2qKq8qVezStepCImIiIiIHENDYxiu2O6P3ETHRTMpbydPvTaeuqYkZswwB59LzfSdZldd2dprOaV7VIRERERERI7B3RBOQkxzj7Y9ZXwFZbWZpCZUc/WXpx1cnprlAqC6sqNXMkr3qQiJiIiIiBxDQ1MErrgeFqFTfI+3XLmeuMS4g8tTspIBqK62JxpPekhFSERERETkGNyNUSTE9ewUtkuuGcVFM1dyxw/HfWx5ZHQk8dEN1NSYo2wpfS3C6QAiIiIiIoGsoSmajJSeze42dOxg/rd8cKfPuWIbaWhUEXKKRoRERERERI7B3RRDQnzvX8vjimvC3aBxCad0uQgZY8KNMWuMMf/xf59qjHnHGFPgf0w5ZN27jTE7jDHbjDEXHLJ8mjFmg/+5PxhjjH95tDHmOf/y5caY3F58jSIiIiIiPdbQHIurT4pQC+7GyF7fr3RNd0aE7gC2HPL9d4H3rLWjgff832OMGQ9cA0wALgQeMcYcmHT9UeBWYLT/z4X+5TcDNdbaUcDvgF/16NWIiIiIiPQyd3McCfHeXt+vK66VhiYVIad0qQgZY3KAi4HHD1n8KeAp/9dPAZcdsvxZa22rtbYQ2AHMNMZkA4nW2qXWWgs8fdg2B/b1InDOgdEiERERERGnWK+loSUBl6v3Z3dLiGvD3RTd6/uVrunqiNCDwP8Bh1bhLGttKYD/MdO/fAhQdMh6xf5lQ/xfH778Y9tYazuAOiCtqy9CRERERKQvNDc047XhJCT0/r5d8R24m2J7f8fSJcctQsaYS4Bya+2qLu6zs5Ece4zlx9rm8Cy3GmPyjTH5FRUVXYwjIiIiItIzDbW+2eJcib1/spIroYOGZhUhp3RlRGg2cKkxZjfwLHC2MeYfQJn/dDf8j+X+9YuBoYdsnwOU+JfndLL8Y9sYYyKAJKD68CDW2sestdOttdMzMjK69AJFRERERHrKXdMEQEJC+HHW7L6EeC/u5vhe3690zXGLkLX2bmttjrU2F98kCO9baz8HvA7c4F/tBuA1/9evA9f4Z4LLwzcpwgr/6XNuY8ws//U/1x+2zYF9Xek/hm6zKyIiIiKOaqhvAcCV1PvTXLtc0NiagNfT+xMxyPGdyCd6H/C8MeZmYC/wGQBr7SZjzPPAZqAD+Kq11uPf5svAk0As8Ib/D8Bfgb8bY3bgGwm65gRyiYiIiIj0CndtKwCuxN6f3c3l8j021DaQmJbY6/uXY+tWEbLWzgfm+7+uAs45ynr3Avd2sjwfmNjJ8hb8RUpEREREJFA01LcBkJAY1ev7diX6Ts5qqG1SEXJAd+4jJCIiIiISUtz17QC4kmN6fd8Hrjty1zb1+r7l+FSERERERESOoqG+A4CEpN6f3e3AdUfumpZe37ccn4qQiIiIiMhRuOt9Exm4UuJ6fd+uJN/pdu661l7ftxyfipCIiIiIyFE0NPgmMk5I7v1prl1J0b5j+E+/k/6lIiQiIiIichRuN0SGtxEdF93r+05I8l135FYRcoSKkIiIiIjIUTQ0GhJiGvtk364U33VH7nrPcdaUvqAiJCIiIiJyFO6GcFxxfVWEfKfbNbh1Q1UnqAiJiIiIiBxFQ2M4CbF9M6tbfKKvCLndfbJ7OQ4VIRERERGRo3A3RuKK65siFB4ZTlx0o4qQQ1SERERERESOoqEpioTYtj7bvyu2EXeDfiV3gt51EREREZGjcDfF4IrvyyLURENjeJ/tX45ORUhERERE5CgammNIiO/os/274ppxN0b22f7l6FSERERERESOwt0chyuh72Z1S4htw90Y1Wf7l6NTERIREREROYqG5ngS4vuuCCUmtFLXENNn+5ejUxESEREREelER1sHLe2xuFx9d4zBWS2UVKX13QHkqFSEREREREQ60VDru5FqQkLfHWPYUC/ldRm0NPbNFN1ydCpCIiIiIiKdcNf4ipArse9mdRs6LAKA4oKyPjuGdE5FSERERESkEw11zUDfFqFhI+IB2Lujus+OIZ1TERIRERER6YS71ne6WkJi301vPWxUKgBFhY19dgzpnIqQiIiIiEgnGup9N1J1JfXd9NY5owcBsHeP715Fv/vufD54aU2fHU8+oiIkIiIiItIJd52vCCUk9l0Rio6LJiu5nKLiMNpb2/nO/afz61/33Q1c5SMqQiIiIiIinWio9xUSV3Jsnx5naEYle0vi2LZqN+2eKD7cMAZPu6dPjykqQiIiIiIinXK7fWUkIalvi9Cw7HqKypLZkF8BQH1zEhuX7ejTY4qKkIiIiIhIp9z1XgBcKfF9epyhQ1rZW57FhnVtGOM75uJ39/fpMUVFSERERESkUw0Nvse4xLg+PU5eLjS0uPj3e0OYMGwnOWklLF7SdzPViY+KkIiIiIhIJ9xuSIhxExbet78yf+bmsURFtLJxz2gmjS5n1qQ9rNw0pE+PKSpCIiIiIiKdamgMIyGmqc+PM3jkIG64eDkAkya0MyS7jfK6lD4/bqhTERIRERER6YS7IQJXXN8XIYDv3DOcYRnFnHtxJhnpFndzIq1Nrf1y7FAV4XQAEREREZFA1NAUQUJsS78ca+TJw9lT7vt69bJKACpLqhkyKrtfjh+KNCIkIiIiItIJd2MUrrj+H5XJyPLdwLWipL7fjx1KVIRERERERDrR0BxFQlx7vx83Pct336LK/Y39fuxQoiIkIiIiItIJd1Msrvj+L0IZ2QkAVOzvn9PyQpWKkIiIiIhIJxqaY0iI9/T7cdOzkwCoLO//EhZKVIRERERERDrhbo7HleDt9+OmDkrBGC8VFbbfjx1KVIRERERERA7j9XhpaEnA5er/MhIeGU5qQg2VVfpVvS/p3RUREREROUxdZT3WhpHi0H1NM5JqqaiKdObgIUJFSERERETkMDXlvqmrU1Kdue1menIDlTUxjhw7VKgIiYiIiIgcpqbCN3V1SpozozIZKc1U1CQ4cuxQoSIkIiIiInKY2qpmAFLSnRmVSU9to7I+yZFjhwoVIRERERGRw9RUtgKQkh7ryPEz0r1U1qfi9fT/rHWhQkVIREREROQwNdW+e/ikZLocOX56Oni8EdRV1jty/FCgIiQiIiIicpiaat9ITEpmoiPHz8jyXZtUsa/GkeOHAhUhEREREZHD1NRAZHgbca44R46fkRUNQGVpgyPHDwUqQiIiIiIih6mpDSMloQ4TZhw5fvqgeAAq9jc5cvxQoCIkIiIiInKYmroIUhLcjh0/Y7DvlLzK8jbHMgQ7FSERERERkcPU1EWT4mp07Pjpg1MAqCj3OJYh2KkIiYiIiIgcpqY+lpTEFseOH5cYR1x0I5WVjkUIeipCIiIiIiKHqXHHk5Lk7Glp6Yk1VFRFOJohmKkIiYiIiIgcpqbBRUpyh6MZMpLrqaiKdjRDMFMREhERERE5hNfjpbYxiZRk62iO9OQmKmudmb47FKgIiYiIiIgcwl3txmvDSUlxNkdGagsVtc7c0DUUqAiJiIiIiByiptw3bXZKarijOdJTPVTWJzuaIZipCImIiIiIHKKmogGAlLQoR3NkZFgaWly0NDo3e10wUxESERERETlEVXkTAKkZMY7mSM/w/apeWVLjaI5gpSIkIiIiInKIsn3NAGTluBzNkZHlG5Gq2FfnaI5gpSIkIiIiInKIsv3tAAwaluZojvSsWAAq9zc6miNYqQiJiIiIiBxi/36IjmwhMc3ZGdsysn0jUhVlukaoL6gIiYiIiIgcoqwigqzkSkyYcTRH2iBfEauudPbGrsFKRUhERERE5BBllTFkpTh/XU5yZhIANTXO3tg1WKkIiYiIiIgcYn+li0HpDU7HIDI6kvjoBmprnU4SnFSEREREREQOUVaTTFZ6q9MxAEiOd1NT6+yNXYOVipCIiIiIiJ+n3UNFfRpZmV6nowCQ4mqgtj7S6RhBSUVIRERERMSvqrQajzeCrCxnJ0o4IMXVRE19tNMxgpKKkIiIiIiIX1lRDQCDhkQ5nMQnObGFWnes0zGCkoqQiIiIiIhf2T7fJAlZg+McTuKTktROjTvB6RhBSUVIRERERMRv/75mALJyXA4n8UlO9FDTEBhZgo2KkIiIiIiIX1lpOwCDhqU5nMQnJcVS35yEp93jdJSgoyIkIiIiIuJXWgoxkc0kpiU6HQWA5GTfpA11lfUOJwk+KkIiIiIiIn5790UzLHM/JixAZo1L9d1DqLbS7XCS4KMiJCIiIiLit7fUxbCsaqdjHJSS5ruHUE1Fo8NJgo+KkIiIiIiI396ydIYNbnI6xkHJaTEA1FY1O5wk+KgIiYiIiIgArU2tlNYMYtjQwJmYICXddw+hmspWh5MEHxUhERERERFg384yAIYND3c4yUeS0333EKqpbnc4SfBRERIRERERAYp2+K4NGpoX73CSj6Rk+mavq63xOpwk+KgIiYiIiIgAewsbABg2KsXhJB+JT4onPKyDmhqnkwQfFSEREREREWDv7g4Aho4e5HCSj5gwQ0pCLbV1+rW9t+kdFREREREB9haFkZFYQawr1ukoH5ORVMe+/dFOxwg6KkIiIiIiIsDekliGZVU4HeMIsyaXsHjdaLweXSfUm1SERERERESAfeVJDMl0Ox3jCGefE0Z1QyrrlxQ4HSWoqAiJiIiIiAAlVWkMzgq8+/Wc9ckRALz/31LfgnY3WOtgouCgIiQiIiIiIa+1qZUqdxqDswPv9LMho7IZPbiQ199MZtULT9L8r2zY+VenYw14KkIiIiIiEvL27/FdGzR4SODcTPVQF56xlwXrpzD9qhvZtm807Pu305EGvAinA4iIiIiIOK2ksAbIIXtIjNNROvWbJ2Zx7fUbKK8IZ9TQ06D8GfB6ICwwi9tAoCIkIiIiIiGvtLgRgMG5iQ4n6Vx0XDSnfWKS75vC02H/o1C3AVKmOJprINOpcSIiIiIS8kqKfJMkZA9LdThJF2TO9T2WzXc0xkCnIiQiIiIiIa+01BIe1kHG0DSnoxxf/FCIz4OKRU4nGdCOW4SMMTHGmBXGmHXGmE3GmHv8y1ONMe8YYwr8jymHbHO3MWaHMWabMeaCQ5ZPM8Zs8D/3B2OM8S+PNsY851++3BiT2wevVURERESkUyX7wxmUXEFY+AAZJ0ieCPXbnU4xoHXlk24FzrbWngxMAS40xswCvgu8Z60dDbzn/x5jzHjgGmACcCHwiDHmwFVcjwK3AqP9fy70L78ZqLHWjgJ+B/zqxF+aiIiIiEjXlJTFMji92ukYXZcwEhp26X5CJ+C4Rcj6NPi/jfT/scCngKf8y58CLvN//SngWWttq7W2ENgBzDTGZAOJ1tql1loLPH3YNgf29SJwzoHRIhERERGRvlZamUh2RsPxVwwUCSPA0wQt5U4nGbC6NPZnjAk3xqwFyoF3rLXLgSxrbSmA/zHTv/oQoOiQzYv9y4b4vz58+ce2sdZ2AHXAADhBU0RERESCQUlVGoOzWp2O0XUJI3yPDTudzTGAdakIWWs91topQA6+0Z2Jx1i9s5Ece4zlx9rm4zs25lZjTL4xJr+iouI4qUVEREREjq+tuY0qdxrZg7xOR+m6hJG+x4ZdzuYYwLp1NZi1thaYj+/anjL/6W74Hw+MyxUDQw/ZLAco8S/P6WT5x7YxxkQAScARJ2laax+z1k631k7PyMjoTnQRERERkU5VldYAkJE5QCZKAEjI9T2qCPVYV2aNyzDGJPu/jgXOBbYCrwM3+Fe7AXjN//XrwDX+meDy8E2KsMJ/+pzbGDPLf/3P9Ydtc2BfVwLv+68jEhERERHpU5WldQCkpUc4nKQbwmMgdoiK0AnoyqedDTzln/ktDHjeWvsfY8xS4HljzM3AXuAzANbaTcaY54HNQAfwVWutx7+vLwNPArHAG/4/AH8F/m6M2YFvJOia3nhxIiIiIiLHU1XWBEB6VqzDSbopYYSK0Ak4bhGy1q4HpnayvAo45yjb3Avc28nyfOCI64ustS34i5SIiIiISH+qKm8GIC0rzuEk3eQaCaVvO51iwBpAJ0KKiIiIiPS+qsp2ANIGJTqcpJvihkFzCXg7nE4yIKkIiYiIiEhIq6zwzRaXNijZ2SDdFZPle2ytdDbHAKUiJCIiIiIhraoK4qIbiXUNsGuEDhShljJncwxQKkIiIiIiEtKqaiJIc9U6HaP7YjJ9jypCPaIiJCIiIiIhraomirREt9Mxuu/giFD5sdeTTqkIiYiIiEhIq6qNJS2pyekY3adT406IipCIiIiIhLTKugTSU1qcjtF9kYkQFqURoR5SERIRERGRkFZVn0RaarvTMbrPGN+okEaEekRFSERERERClqfdQ01jMmmp1ukoPROTqSLUQypCIiIiIhKyaivqsDaMtDTjdJSeicnSqXE9pCIkIiIiIiGran8dAOkZEQ4n6SGdGtdjKkIiIiIiErIqSxsASMuMdjhJD8VkQms52AF6ap+DVIREREREJGRVlTcDkJYZ53CSHorJAm87tNc6nWTAURESERERkZBVVdEGQNqgBIeT9FB0pu+xWafHdZeKkIiIiIiErKrKDgDSByc7G6SnYgf5Hlv2O5tjAFIREhEREZGQVVkJEeHtuFJcTkfpmbgc32NTsbM5BiAVIREREREJWVXVYaS5ajBhA3T67INFqMjZHAOQipCIiIiIhKyqmijSEuudjtFzEfEQlaIi1AMqQiIiIiISsqpqY0hPanA6xomJGwqNKkLdpSIkIiIiIiGrqi6etOQWp2OcmLihGhHqARUhEREREQlZlXWJpKW0OR3jxKgI9YiKkIiIiIiEJOu1VLlTSEv1Oh3lxMQPhbZq6GhyOsmAoiIkIiIiIiGpobaBdk8UaWlOJzlBcUN9jxoV6hYVIREREREJSVWldQCkZ4Q7nOQEqQj1iIqQiIiIiISkqjI3AGkZUQ4nOUEHipBmjusWFSERERERCUmV+xsBSMuMdTjJCYrLAQw07nY6yYCiIiQiIiIiIamqohWAtKwEh5OcoPBoSBwDNWudTjKgqAiJiIiISEiqqugAID07yeEkvSB1OlSvcjrFgKIiJCIiIiIhqarKYoyXlKxkp6OcuNTp0LwPmvc7nWTAUBESERERkZBUUWlIia8lPHKAzxoHviIEGhXqBhUhEREREQlJpWXRDE6rcjpG70iZAhioznc6yYChIiQiIiIiIamkIoHBGXVOx+gdkQmQNA72vwfWOp1mQFAREhEREZGQtK8ilcFZzU7H6D0jb4GKRbDxZ04nGRBUhEREREQk5Hg9XkprMhk8yON0lN4z9g7Iux42/Bj2vuB0moCnIiQiIiIiIaeiqAqPN4LBQ4zTUXqPMTDzMUg/HZbeAPXbnU4U0FSERERERCTklOz2TZIwOCfa4SS9LDwa5rwA3jbY9YTTaQKaipCIiIiIhJySvW4ABg9LcDhJH4gbDIPOgz3PaeKEY1AREhEREZGQU1LkmyRhcG6qw0n6yPBroHE3VK1wOknAUhESERERkZBTss8LwKDcDIeT9JGcyyAsEopfdTpJwFIREhEREZGQU1IaRmZSBZHRkU5H6RtRSZA4HmrXO50kYKkIiYiIiEjIKSmLYXBaldMx+lbiSVC3xekUAUtFSERERERCTkmFi8EZbqdj9K2kcb7rhDqC6KaxvUhFSERERERCzp6yTIYNCfKCkDgOsODe5nSSgKQiJCIiIiIhxV3tpsqdRl6u1+kofStpnO9Rp8d1SkVIRERERELK7i2lAOSOiHI4SR9zjQETBvUqQp1RERIRERGRkLJ7ey0AuaOTnA3S18KjIX6ERoSOQkVIREREREJK4Q7ftUF547IcTtIPksZB/VanUwQkFSERERERCSm7d1viohtJH5LmdJS+lzASGnaBtU4nCTgqQiIiIiISUgr3xpCXVYIJM05H6XsJI8DTBC3lTicJOCpCIiIiIhJSdpckkzu4xukY/SNhhO+xsdDZHAFIRUhEREREQsruskHkDg3yewgdcKAINexyNkcAUhESERERkZBRW15HbWMyebkhcs1MfK7vUUXoCCpCIiIiIhIydm/dD0DuiGiHk/STiFiIHawi1AkVIREREREJGYXbfNcG5Y5JdjZIf0rIUxHqhIqQiIiIiISM3TtbAMgbN8jhJP0ofoSKUCdUhEREREQkZOzebXDF1pOSlex0lP6TMAKaisHT6nSSgKIiJCIiIiIho7AohrxBpaFxD6EDXCMBC427nU4SUFSERERERCRk7C5JIXdwrdMx+pdrjO+xfruzOQKMipCIiIiIhATrtRTuzyY3p8XpKP3LNdr36FYROpSKkIiIiIiEhOr9NTS0uMjLczpJP4tOheh0FaHDqAiJiIiISEjYvbUMgNyRMQ4ncYBrjE6NO4yKkIiIiIiEhF3baoEQu4fQAYljNCJ0GBUhEREREQkJ61a3Eh7Wwdipw5yO0v9cY6C5BNobnE4SMFSERERERCQkrFofz4ThO4l1xTodpf8dmDnOXeBsjgCiIiQiIiIiQc96Lflb85g2vtzpKM5I9Behus3O5gggKkIiIiIiEvSKtpdQWZ/OtGlep6M4I3EcRLigconTSQKGipCIiIiIBL1Vi4oAmHZamsNJHBIWARmzoXyh00kChoqQiIiIiAS9VStaCA/r4OTZI52O4pzMM6FuE7RUOp0kIKgIiYiIiEjQ27YjmpHZRaE5UcIBmWf6HisWO5sjQKgIiYiIiEjQ21eWQE5mjdMxnJU6HcJjYP+7TicJCCpCIiIiIhL09lWmkDOoyekYzgqPhpzLofApaAvxUoiKkIiIiIgEOa/HS0l1FkOyO5yO4rzx34GOBih41OkkjlMREhEREZGgVr63kg5PJENyjNNRnJdyMmRfBFvuh5YQvaeSn4qQiIiIiAS14p2+WdJyhsU4nCRAnHI/dDTCqjucTuIoFSERERERCWr79rgBGDLc5XCSAJE0HiZ8D/Y8CzVrnU7jGBUhEREREQlqxXtbAMgZme5wkgAy5mtgImD3v5xO4hgVIREREREJavuKLRHh7WQOUxE6KDoNsi+EPc+A9TqdxhEqQiIiIiIS1PaVRjA4tYywcP3q+zG510JTMVQscTqJI/Rfg4iIiIgEteL9cQxJ131zjjDkk2DCYf87TidxhIqQiIiIiAS1feXJ5AxqcDpG4Il0QfIkqFzmdBJHqAiJiIiISNCyXktxZSZDBrU5HSUwpZ0KVStC8johFSERERERCVr1VfU0tiaQk2OdjhKY0mdBex3Ub3M6Sb9TERIRERGRoFW8owKAIUOjHE4SoNJm+R5D8PQ4FSERERERCVr7dtcBMGR4vMNJAlTiGIhMgioVIRERERGRoLFvbxMAOSNSHU4SoEwYpE6DmrVOJ+l3KkIiIiIiErSKizwADB6R6XCSAJY8GWo3gtfjdJJ+pSIkIiIiIkFrX0kYGYkVRMdFOx0lcCVPBk8TNOxyOkm/Om4RMsYMNcZ8YIzZYozZZIy5w7881RjzjjGmwP+Ycsg2dxtjdhhjthljLjhk+TRjzAb/c38wxhj/8mhjzHP+5cuNMbl98FpFREREJMQUl8YwJL3K6RiBLWWy77F2vbM5+llXRoQ6gG9ba8cBs4CvGmPGA98F3rPWjgbe83+P/7lrgAnAhcAjxphw/74eBW4FRvv/XOhffjNQY60dBfwO+FUvvDYRERERCXH7yhPJyap3OkZgSxzvu1ZIRejjrLWl1trV/q/dwBZgCPAp4Cn/ak8Bl/m//hTwrLW21VpbCOwAZhpjsoFEa+1Sa60Fnj5smwP7ehE458BokYiIiIhITxVXpjNkUIvTMQJbRCy4RqsIHYv/lLWpwHIgy1pbCr6yBBy4Am0IUHTIZsX+ZUP8Xx++/GPbWGs7gDogrZPj32qMyTfG5FdUVHQnuoiIiIiEmNamVirr08kZ4nU6SuBLngw165xO0a+6XISMMQnAS8A3rLXHGl/sbCTHHmP5sbb5+AJrH7PWTrfWTs/IyDheZBEREREJYft2lgEwJCf8OGsKKVOgsRDaap1O0m+6VISMMZH4StA/rbUv+xeX+U93w/9Y7l9eDAw9ZPMcoMS/PKeT5R/bxhgTASQB1d19MSIiIiIiB+ze5pskYfjIBIeTDACp032P1audzdGPujJrnAH+Cmyx1v72kKdeB27wf30D8Nohy6/xzwSXh29ShBX+0+fcxphZ/n1ef9g2B/Z1JfC+/zoiEREREZEeKSxoACDvpHSHkwwAqdN8j9WrnM3RjyK6sM5s4PPABmPMWv+y7wH3Ac8bY24G9gKfAbDWbjLGPA9sxjfj3FettQfuzvRl4EkgFnjD/wd8Revvxpgd+EaCrjmxlyUiIiIioa5wl4fwsA6Gjsl2Okrgi06D+OEqQoey1i6m82t4AM45yjb3Avd2sjwfmNjJ8hb8RUpEREREpDcU7olkaHopEVFDj7+y+E6Pq853OkW/6dascSIiIiIiA0VhcSJ5gyudjjFwpE6Dhp0hM2GCipCIiIiIBKXC0kzychqcjjFwpJ3qe6xY4myOfqIiJCIiIiJBp9ndzP7aLPJyPcdfWXwyTofwWCh9y+kk/UJFSERERESCzu4tvru05I2IdDjJABIeA1lnQembTifpFypCIiIiIhJ0Crf6bkmZNybR4SQDTPaF4C4A906nk/Q5FSERERERCTo7tzcBkDcu0+EkA0z2hb7Hfa87m6MfqAiJiIiISNDZvMWQHF/LoFwVoW5JHA1ps2D7w+AN7uurVIREREREJOhs3J7ExLw9mLCj3Q5Tjmr8XdCwC4pfdjpJn1IREhEREZGgYr2WjYXDmTimzukoA9OQT4FrNGy8F6zX6TR9RkVIRERERIJKaWEZtY3JTBhvnY4yMIWFw6SfQO062P0vp9P0GRUhEREREQkqG1fsA2Di1CSHkwxgw6+BlFNg/Q/A2+F0mj6hIiQiIiIiQWXjWjcAE2bkOJxkADNhMPGH0LgHSv7ndJo+oSIkIiIiIkFl0+ZwMpMqyBia7nSUgW3IJRCbDTv+7HSSPqEiJCIiIiJBZWNBChNHFDkdY+ALi4ARN0PJG9C41+k0vU5FSERERESChtfjZdPuXCaOcTsdJTjkXQ9Y2Pdvp5P0OhUhEREREQkae7bso7E1gQkTdf+gXpE4GhJGQsmbTifpdSpCIiIiIhI0Nq0qBWDi1BSHkwSR7Auh7H3wtDqdpFepCImIiIhI0Ni4tgmA8TOGOZwkiAy+EDxNULHY6SS9SkVIRERERILGxs2R5KSVkJypewj1msx5EBYJpW85naRXqQiJiIiISNDYtCONiSP3OR0juEQmQNosKJvvdJJepSIkIiIiIkGho62DLUW5TBjT6HSU4JM1D2pWQVud00l6jYqQiIiIiASFnRv20toew8RJEU5HCT5ZZ4H1BtV1QipCIiIiIhIUNq0qB2DiKakOJwlCabMgLArK5zudpNeoCImIiIhIUNi4rgWAcTOGO5wkCEXEQvppUPaB00l6jYqQiIiIiASFjVuiGTFoD/FJ8U5HCU6Z86BmDbTVOp2kV6gIiYiIiEhQ2LQzg4kj9zsdI3gduE6ofJHTSXqFipCIiIiIDHitTa1s3zecCWObnY4SvNJPhbDooLlOSEVIRERERAa87Wv20OGJZMLESKejBK/wGMg4PWiuE1IREhEREZEBb+v6KgDGT01zOEmQyzwLatZCc5nTSU6YipCIiIiIDHjbNrcCMPrkHIeTBLmhlwMWil5yOskJUxESERERkQFvW0EEQ1JLSUhJcDpKcEueCEnjYe9zTic5YSpCIiIiIjLgbStMYezwUqdjhIZhV/tmjmva53SSE6IiJCIiIiIDmvVathXlMHZEg9NRQsPwawELO/7sdJIToiIkIiIiIgNa+d5K6puTGDvGOh0lNCSOhpzLYdtD0F7vdJoeUxESERERkQFt27oSAMZOiHc4SQiZcDe018L2R5xO0mMqQiIiIiIyoG3bWAfA2JOzHE4SQtJmwOCLYfN90FrldJoeURESERERkQFt5w4vkeFtDBs72OkooWXKr6DDDRt/7nSSHlEREhEREZEBbU9RFEMzSgmPDHc6SmhJngB5N0DBo9BS7nSablMREhEREZEBbU+Ji+GDqp2OEZrGfwe8bbD9YaeTdJuKkIiIiIgMaHvK0hk+RFNnOyJxLOR8yleEOpqcTtMtKkIiIiIiMmC1NbdRWpPF8KEep6OErrFfh7YaKH7d6STdoiIkIiIiIgNW0fZSrA1jeK6uD3JM5lyIy4Hd/3A6SbeoCImIiIjIgLWnwHdt0PCRCQ4nCWEmDHKvg9I3B9SkCSpCIiIiIjJg7d7huzZo+OhUh5OEuNzPgfXA3hecTtJlKkIiIiIiMmDt2e3BGC9Dx2Q7HSW0JU+EpPGw90Wnk3SZipCIiIiIDFh7iiLITikjKjbK6Sgy9EqoWAjNZU4n6RIVIREREREZsPbsiyd3UIXTMQRg2GfAeqH4FaeTdImKkIiIiIgMWHv2pzF8sNvpGAKQNMF3X6EBcnqcipCIiIiIDEiedg9FldkMH9rudBQBMMZ3elz5B9AS+KN0KkIiIiIiMiCV7i6nwxPJ8Fz9ShswDp4e96rTSY5L/9WIiIiIyIC0Z5tv1GH4iFiHk8hByZMhYdSAmEZbRUhEREREBqQ9O33XBg0fneJwEjnIGMi9Fva/Cw27nU5zTCpCIiIiIjIg7Sn0XRs0/CTdQyigjLzFV4h2POZ0kmNSERIRERGRAWnP3jDSXFXEJ8U7HUUOFT8UhlwKOx8HT4vTaY5KRUhEREREBqTdxXEMzyp3OoZ0Zszt0FoBBX92OslRqQiJiIiIyIC0pzSF4dl1TseQzmSdBVlnw6afQ3tg3udJRUhEREREBhzrteytGMTwnFano0hnjIGTfwmtlbDpl06n6ZSKkIiIiIgMODVltTS1xjNsmHU6ihxN+kzI/RxsfQDqC5xOcwQVIREREREZcPbt8t1DaMjQKIeTyDFN/Q2Ex8Dqbzmd5AgqQiIiIiIy4BQX+q4Nysl1OZxEjil2EEz4HpT8B8oXOp3mY1SERERERGTA2bfXNy3zkBG6mWrAG/N1iB0Ca74DNnBOZVQREhEREZEBp7jIgzFesnMznY4ixxMRCxO/D1XLoGKJ02kOUhESERERkQFnX0kYmUmVRMXqGqEBIe8GiEqBbb93OslBKkIiIiJBbv3i7Tz+i0XsL9SNJyV47NsfS05GpdMxpKsi4mDUrVD8MjTucToNoCIkIiIS1J64bxEnnzGGW75/BrfftNPpOCK9prg8iSGZgXmjTjmK0V8FDGx/2OkkgIqQiIhI0Nq9qZg77pnCvJPX8PWrF/Di/NNYv3i707FEesW+ynRyslucjiHdET8Uhl4BOx6H9gan06gIiYiIBKsf37Ubaw1PPpPBj383mcTYOr777Tqs19JU38QXL13Esw996HRMkW5rdjdT3ZDKkMGBMwOZdNHYb0B7LRQ+7XQSFSEREZFgVFdRxwvvncLnPrGa4eNySM1O4WffXMsbK2bw/VsWcOV5m/jrv8/gs3fM4oZPLOb2qxfQ0dbhdGyRLtm303e9W87QCIeTSLelz4LUGbD9D2C9jkZRERIREQlCz/5pHc1tcdz85fSDy27/2ZlcctoKfvnEPN5cOY0H717AJaet5MX3p/Lw83N56bEVDiYW6bo9BVUADBke53AS6TZj4KRvQP02KH3b2Sg2gG5q1B3Tp0+3+fn5TscQEREJSKeN20BDcwzrd43ChJmDyxvrGlk1fwejJw0ie0QWAJ52D+Pz9pIQ20L+tpM+tr5IIPr57fP50R/PpHJfHanZuqHqgONpg9dzIWkCnP1Onx7KGLPKWju9s+c0IiQiIjJA1ZTV8r2b53PhzHyevn/xweXleytYvm0CV12y74hSE58Uz5mfOvlgCQIIjwznrq8Us3rHOBb9e31/xRfpsYVLXUzKLVAJGqjCo+Ckb8P+d6F88fHX7yMqQiIiIgPQlhU7mTS+mV8/OYf8rXl866fjaKxrBOCtF7dhbRif+HTWcfbykWu+PJXwsA7e/V9NX0UW6RXtre18uHEsZ87Y73QUORGjvwwxWbD+h+DQGWoqQiIiIgPQb35aQl1TAsvfKeC1f+2jyp3GE7/xnTL+xhuGrORyps4d2+X9JaQkMGXkdhavSOqryCK9YvX8bTS2JjB3XpTTUeRERMTBhB9A+XzY85wjEVSEREREBpim+iZeeG8KnzlnLdPOHsfsSyYzZ+I67vnDRF54dClvLh3PhadvJyy8ez/mZ59SwfItY2hvbe+j5CInbuE7lQCccdEoh5PICRv9Zd8Mcqu+Dq1V/X54FSEREZEB5pW/rqGhxcUNN7sOLnv8SReJcY1c9ZXTaOuI5JYvJ3Z7v3PmRtHUGs/ahQW9GVekVy1bGcPIQXvIys1wOoqcqLBwOPVxaKuBVd/s/8P3+xFFRETkhPzlyXhyM4s449LJB5eNnTaC5SvjePSeheze0crsSyYfYw+dm31+HgCL3yvvtawivW3l5qHMnFjsdAzpLSmTYfx3YfffoeTNfj20ipCIiMgAsur9LSxYP4Wv3bDziFPfMoamc9uPziQ9J61H+x48chAjBu1h8dKY3ogq0utKd5VRVDmEmdN189+gMvEHkHgSrLwN2hv67bAqQiIiIgPIb++rxhVbzxfvmton+589pYgl60dgvQPzPoMS3FbO3w3AjNmaNjuohEfDzL9A4x7fLHL9REVIRERkgCjaVsLz78/ki5evJimjb2Z3mzPbS1ltJjvX7+2T/YuciJXLmgkP62DqmZooIehkzoFRt8L2h6BuS78cUkVIREQkgP3h+wv4yZfnY72Wh36xHa83jDt+0He/BM45bzAAi98u6rNjiPTUijUJTMrdQVxinNNRpC9M/jlEJMDqb/XL4VSEREREAtSi19fxjV+ewT1/msctly3msRencuVZyxk+LqfPjnnS9BGkxNeweLFOjZPAYr2WFVtGMWOSJvMIWjEZMPFHUPomlLzR54dTERIREQlAXo+Xm7+cSF5WEdec8yF//fcZxMc08YOfpvfpccPCw5g9uYD3lufi9Xj79Fgi3bFj3R5qG5OZOdM4HUX60pivQcIo36iQt2/vaaYiJCIiEoA2L99JQUke379jL/9441QKNxZTXDGISbPH9Pmxr/lMO7vLh/LeC2v6/FgiXbVy4T4AZp6Z5XAS6VPhUXDKA1C/FQr+1KeHUhESEREJQAveKgHgrEtyCY8MJ3dCDiasf/5P+JVfmk56YiWP/LFv/2+sSHesWN5BbFQT42eOcDqK9LUhn4Ssc2DDj6G1us8OoyIkIiISgBYsimJo+j5yx/fd9UBHEx0Xzc2f3sjrS2awa4Nmj5PAsGJtKtPGFBARFeF0FOlrxsApv4X2OthwT58dRkVIREQkwFivZcHqUcydVthvo0CHu/37Y4mKaOOn31UREue1t7azZscoZkyucTqK9JeUyTDyFij4Y59Np33cImSMecIYU26M2XjIslRjzDvGmAL/Y8ohz91tjNlhjNlmjLngkOXTjDEb/M/9wRhj/MujjTHP+ZcvN8bk9vJrFBERGVC25u+ivC6DeXOdm7ltyKhsvnLVcv7+xmn86/dLaG/VaXLinOVvb6GlPZbZZ0Q7HUX60+SfQkQ8rLoDbO9P3tKVEaEngQsPW/Zd4D1r7WjgPf/3GGPGA9cAE/zbPGKMCfdv8yhwKzDa/+fAPm8Gaqy1o4DfAb/q6YsREREJBgve9F0UPveiYY7m+O4vJzJiUBHXfWM2t16xzNEsEtre/V81xng5+/JxTkeR/hSTCSf/Eva/A1se6PXdH7cIWWsXAodfpfQp4Cn/108Blx2y/Flrbau1thDYAcw0xmQDidbapdZaCzx92DYH9vUicM6B0SIREZFQtGBhBINTSxk52dkilDE0na17hvKlyxfyjzdnUbJzv6N5JHS9tziFaaO2kpKV7HQU6W+jvwxDr4R1d0P54l7ddU+vEcqy1pYC+B8z/cuHAIfeirrYv2yI/+vDl39sG2ttB1AHpPUwl4iIyIBmvZb5q0Yy95Rdjl0fdKjwyHDu/NFwPN5wHrt/q9NxJAS5q90s2zKec+foRqohyRg49XGIz4Ul10BLRa/turcnS+jsX2x7jOXH2ubInRtzqzEm3xiTX1HRe2+CiIhIoChYu5v9tVnMm+txOspBo6YM56KZq/jL82N1k1Xpdwv/s5UOTyTnXpTodBRxSlQSzHkBWith6fW9dr1QT4tQmf90N/yPByp6MTD0kPVygBL/8pxOln9sG2NMBJDEkafiAWCtfcxaO91aOz0jI6OH0UVERALXgjd8J1bMvaj/p80+lquuaKWkOpt1i7Y7HUVCzLtvNhET2czsT4x3Ooo4KXUqTPsdlL4JW+7vlV32tAi9Dtzg//oG4LVDll/jnwkuD9+kCCv8p8+5jTGz/Nf/XH/YNgf2dSXwvv86IhERkZDz9ruRDE4tZczUPKejfMwFV44B4M1XdZ2Q9K93PxzMnEmbiYmPcTqKOG3UbTDsM7Due1Dx4QnvrivTZz8DLAXGGmOKjTE3A/cB5xljCoDz/N9jrd0EPA9sBt4EvmqtPTC2/2XgcXwTKOwE3vAv/yuQZozZAXwL/wx0IiIioaa9tZ13VoznE3MKAuL6oEMNystk6sgtvPFeyvFXFukl+wvL2bhnNOec6XY6igQCY2DmXyBuGCy5FtrqTmh3x701r7X22qM8dc5R1r8XuLeT5fnAxE6WtwCfOV4OERGRYLf0zc3UNZ3MRRdHOR2lUxfNK+NXf5tDbXkdyZlJTseREPD+6wVAJudenHncdSVERCXB7GfhndMh/3Y4/eke76q3J0sQERGRHvrfqzVEhLdz7hWBeS3E+Rcn4/FGsPA/mj1O+sf8Dzwkx9cyde5Yp6NIIEmfCRN+ALv/Dntf6PFuVIREREQCxP/mD+aMSRtJTAvM2bFOPf8kYiKbmf9es9NRJETkb8xg+tidhEeGOx1FAs3E70PaTFhxGzTt69EuVIREREQCQNG2EjbsHsMnzj2xc977Ukx8DKdN2Mr85Vnd2s56LVUlnU4IK3JUrU2tbNw9klMm6fog6URYJJz2d/C0wKIrwdPa/V30QSwRERHppjdf3AHAJ64MrGmzDzfv9DrW7hpLdWlNl7f5wa0LGDTMxaLX1/VhMgk2G5ftot0TxbQZgXnNnASAxDFw2tNQtQxW3gbdnHhaRUhERCQA/O/NKIZlFDNuxkinoxzTWRekYG0YC/6z7WPLm93NWO+Rv4Tkv7uZ+544A683jM/elNGtAiWhbdWHFQBMmzPE4SQS0IZdAZN+AruehK2/69amKkIiIiIOa6pv4t38CXzijF0BN2324WaeexLZKfv52a+S6GjrAKBwYxGDs1uZNmYri/+9HoCOtg5+/e35zLtkGNkp5bzz/HqKqwbzyC80KiRds3o1JMXVMWLSMKejSKCb+EMYegWsubNbkyeoCImIiDjsid+spKHFxXU3Jjsd5bii46L5w72FrNk5jl/ftZiOtg6uv7Yar9dQXe/irMvHcdf185lz8la+89t5nDtjE4sWdHD2lVM5a8oa/vZiHl6P1+mXIQPAqo3pnDIm8P/ngAQAE+Y7RS5jNnx4Hex/t0ubqQiJiIg4qKOtg/v/PJLTx69nzicnOx2nS6740iw+c9ZSvv+HeYzM2c/ijSfz8M83sH5LIudOW8v9f59HaVUy/3xwCa8uOpW8iUMBuOn6JnbtH86i19c7/Aok0DXWNbJ212hmnRK4k4dIgImIg7mvQ+JJsPByqMo/7iYqQiIiIg5Z/O/1TB1TyJ6KHL57Z4vTcbrMhBn+9eYMvnbVAmKi2nnpz8v4/LfmkJiWyP+WTqdyXzV7ynP47B2zP7bdp2+eSmJsHU/8pcGh5DJQLH9nOx2eSM44K97pKDKQRKXAvDchOh3mXwT12465uoqQiIiIAzYv38HFVw+nqTWKfz64hEtumOF0pG6JiIrgoefmsq04j0/fOuvgchNmSBuc2uk2cYlxXHPBOl58fyr1VfX9FVUGoMUf1GGMl9POH+N0FBlo4gbDWW8DBt4//5irqgiJiIj0s2Z3M5dfEU5sdCsffBDBZ++YHTLXQXzhS6k0tcbz/J81aYIc3eLlLiblFpCcmeR0FBmIEkfDWW9C27FnqVQREhER6Wc/uX052/fl8c/Hihl2UmhNDXzq+RMYN3QnT/wj2ekoEqA62jpYumkMZ0zf73QUGchST4G5rx1zFRUhERGRfrRt1S7uf/oMbv7kIs75zClOx+l3Jszw+U8XsXTLJHZvKnY6jgSgD9/YREOLi3ln6UaqcoKyzjrm0ypCIiIi/egXPywhOrKVXzx8ktNRHHP1zSMAePHJHQ4nkUD00jO1REe2cOE1k5yOIkFORUhERKSf7Nqwl3++NYvbrlxB5rAMp+M4ZsSkYUwfvZnnXst0OooEGOu1vPzuaC6YuY6ElASn40iQUxESERHpJ3+6fxfGWO78aeiOBh1w1aXl5BeMZ92iY09vK6Fl2ZsbKa4azBWXtTsdRUKAipCIiEg/aG9t5+nXx3PJaasYPHKQ03Ec98W7ppCaUM2372jEeq3TcSQA1FfVc9OX4khzVXHp9TotTvqeipCIiEg/eOOfqymrzeSmm/SjFyAlK5mf3LGB99acwg0XL2HH2j1ORxIHeT1ervvkVgpKhvPik0WaNlv6hf41FhER6WPWa3ngwWgGJZdx0XWhN1Pc0dz2w9P5ypULePmDKVz56RaNDIWwn3xlIf9ZOpMHv7eEeZ+e4nQcCREqQiIiIr3o1ceX8+KjSynbXXFw2cuPLWPhhin8+JvbiIiKcDBdYImMjuSPL8zloXvWsK5wLG8/u8rpSOKAxrpGfv23WVxzzod89Z4znY4jIUT/GouIiPSSV/6yjE/fOuvg9+OG7uTc04v5538nMXF4AV/87ukOpgtc1339VH7461J+9ZtwLvis02mkv7330kZa20/li1+KwYQZp+NICNGIkIiISBe1Nbexd+s+6qvqj3hu9QdbuPGOccwYs4kl/9nAr789n8wUNw89N5cpo3fzwgvhGg06iqjYKL558zY+WDuVle9sdjqO9LP//rsVV2w9Z1wy0ekoEmKMtQPzfNzp06fb/Px8p2OIiEiI2Lx8B5d8KorCsmHERjVxz+0r+Ma9s4mMjuTtZ1Zx5c2jSUmoZ+ECGD4u5+B2TfVNxCXGOZh8YKivqmfYMMu5Mzbz4vzTnI4j/cR6LcMyS5k5YS8vLZh1/A1EuskYs8paO72z5zQiJCIichwFa3Yz+5x0mlujeeQnCzl/5gb+74F5TDupkKvP+ZALr5vK8MwyliwJ/1gJAlSCuigxLZGvXLOGlxeeyqr3tzgdR/rJu8+vprhqMJd8QvcNkv6nESEREZFjaKxrZNbJJZRUpbHywwZGTBoG+CZF+M5PMmlti+TCObt44G/TiE+KdzjtwFZVUs3Jk1qJiWpj9cYUEtMSnY4kfaituY2TxxTR3hHBxh1ZxMTHOB1JgpBGhERERLqpsriKzct38Ml529m8dwT/+vOugyUI4LIvnsq24jx2l+fwp5fPVAnqBWmDU3nuqQoKy3L48e1rnI4jfezx+5aytXgkv/9VuUqQOEIjQiIiIoco2bmfb39pFy/On0GHJ5Iw4+GpB5bxuW/OdjpayLjx4sU89840Cre5GZSX6XQc6QPWa5mQu4uE2BZWbJvgdBwJYhoREhER6YIVb29i8tRIXls0ha9fs4R//f5Dlr21TSWon33/5zm0dUTx429t1U1Wg9Sif69nS9FIvnxTtdNRJIRpHk8RERGgbHcFl1+bSmJcI0s+qGPstHlORwpZo6fmcse18/ndv+bReNESXAkeqmsjuO0rsZx1xVSn40kvePShRpLja7n6y9OcjiIhTEVIRERCnrvazacuKqPaPZJl7+1l7LSxTkcKefc/fSYtrQv588uzSUmoBeC/10Xzmb8tYnthEv+bP5ykjCRnQ0qPlO2u4KX50/nKVUuJS5zrdBwJYTo1TkREgl5HWwf33zWfvKwiPvzvho8919bcxmXnFpC//SSeeWQ9J5+hEhQIwsLDeOTFM2lrhcr6NDas7SAtsZZ/vnkqS7dM5Md3rHU6ovTQEw9uot0TxW3fHOp0FAlxKkIiIhLUmuqb+NSZa7jr/nmU1mTwpa/G0N7qu2eJp93D165dxvtrTuGJXy3jsi+e6nBaOVx4ZDgA2SOyyF8VQ+HWKr50+WIeenYOG5ZsdziddFdteR2P/GMMZ01Zw0kzRjgdR0KcTo0TEZGgYb0Way1h4b7/z9dU38RFc7azeOMp/OmnC8kcFMWnb53FmVM2MCavjmXrh7B935ncfdN8rr9znrPh5bgyhqYDcO/DMTzzZgM/+E4Nry12OJR0yZL/rOeFf9WwZmMS+2sn8MzfKp2OJKIiJCIiwWHPlmIuvbiRjbtHkZJQRUZSLcbA1uLJ/Ov3y7jm9jOxXsuvts7n+X9nMj8/l+FZVfz8+0u58ku6TmEgSc1O4c6b5vPDh+ex7M2NzLpwotORpBOFG4s4/3wPaUkNrC44Ca8Nw+ON4M8/W8icT57pdDwR3UdIREQGtvqqen5x52r++vJE2jsiue2qNdS7w9hfHkXR/kS+fls1n//WHKdjSi9zV7sZPbKZmMg2Fi3yjQDe94MCsgdZxk2IJiExkriESGZfPOngCCHA5uU7eOGpYvJGRPDpm6eQkJLg1EsICvsLyyneWUlmThLDThpycHlVSTWzZ9ZSVptK3qBS8obU8fjzJ+H1eEkbnOpgYgk1x7qPkEaERERkwOpo6+Cqi7bzTv4ZfGLWKu57IJUJp81zOpb0A1eqi/++VMzZlwzmpJMjiAjroKV9Fu2eSKz9qPicMmoLv/xpE3W1bfzuoQSWbpkEjALg0b9uYMnGCR8rStI1rU2tXDp3A2/nTwd8N709OW8bI4fWcPLEFt6an8ru8jG88+I2zrj0ZGfDihyFipCIiAxYv75rMW+tnMdjP1/ILd/XqTahZtrZ41j8znb+8lAp+8ujuPeBbNIHJ7N3exkNda1s31THPQ/kccFnxwEwZkghv7lzPp//6gRe/NtmvvbTubzw6Idc/bXTHXsNXo+X8r2VZA3PwIQZx3J019evW87b+Wfyoy/N55QZsewqaOa1N5LZvCuDlxeOxBgvLzyynDMuPc3pqCJHpVPjRERkQLJey4jsYkYPLeftfN2UUTrX2tTKP36/ggRXOFd+6dSDs9B52j1MG7uD+qZYtuzKIjouut+zNdU3ce6snSzdMonhGcX8/fGqATF68tdfLuKL3zuD735hPr98Yt4Rz+9Yu4eKknpO+8Sk/g8ncphjnRqnsWARERmQVr67md3lQ/nsVc1OR5EAFh0Xzc13n8HVXzv9YAkC37Tcv763gcKyYTz6s2X9nsvT7uHaT2xg2dYJ3H3TfKKj2jnn0+N44x8rP7ZeRVElX7hkEdNGb+G685dQW1533H1bb9/9T+7VH2zhqz+ewbmnrOLnfz6j03VGTRmuEiQDgoqQiIgMSM89XUFkeBufukEzhknPnH/tNM6fns/PHp5EZXHVweVtzW18+N8NvP3MKqpLa4Ajy4X1Wt5/cQ1funwhS/6z/qjHKNtdwY9vm8915y/hH79bgvVavB4vt1+7hNeXnMofvr+IX/x1HstWpTB+WCGf+/JIfnDLfK6/aDF33zSfk8aH8a+3ZpKR0sgL78/gzFPLqCqp7vRYdRV1fPHSReRk7Kdgze4Tf4MO09rUyudvjCTNVcMzr+V+rFiKDEjW2gH5Z9q0aVZEREKHp8NjK4oqrdfjtQteXWuT4mrtJ09f5nQsGeDWLdpmoyOb7aTcbbZ0V5n91++XWFdsnQVrwdqMxHJ73rR8mxRXa99+Jt96Ojz2r79caMcM2XVwnZjIJvu/v684Yt8fvLTGJsfX2PCwdpuZVG7B2tPHr7OnjVtvwdo7P//Bx9bfvrrQJsTUW7A2zVVpwdqLZy23Gz8ssNZa+85zq2xkeKu9bM5S6/V4P7Ztc0OznTFmow0zHTYuusHOOmm9bW9tt+2t7Xb94m12xdubjtimu75z4wcWrH3jHytPaD8i/QnIt0fpE7pGSEREAl5jXSMXzN7Jkk2TMcaLtWGMHbKLN9+KIndCjtPxZIB79/nVfOr6sbhiG6l2JzNj7Fbu/EYTcfER/PhnMezZn4ErrondZUNIddVSVpvJjDGb+Ppttcy7ZASXXlTL7v2D2LS+jewRWbzxj5VMmD6YC85rob0jgv+87mH0lOE89KPFPP1CJpV1ifzk27v4wv/NOWKChK0rdxEeEcbIycOoKas9YqrpB/5vPnf+Zh5funwh3/rBUDbml/Gr3ybQ2hbJusKxvPzYMlpbvFz79dMZOWgPTa3RlNYMAuD/bpjPfU/M7dGkDMve3MjsT4zjpk8u4S+vaWISGTiOdY2QipCIiAS0pvomrjhvE2+vPIXv3rQIYyA93XD91yeTmp3idDwJEhuWbOeaz4YRZiyLVmSSnJn0seerS2v44dfX09wcxvkXhHP1V087WCi2ry7k5FmDmD1xC9Mm1/Prp+YRHdlCa3sMr/xlOZd98dRey+n1ePnGdYt45IXZeLy+yX/HDCnEWrjxyj187/fzAHjlL8v4/cMxJLlaufLT7Sxe5OWxV8/k3q/PP7hOV3W0dTB51B4aW6LZsCWRxLTEXns9In1NRUhERAak+qp6zpu9l/zt43js3g+5+e7OL84W6Q1ejxdPh4fI6Mhub/unny7k9p+eRocnkivmLmXjjiyGZNbybv7UPpkWe8faPSx6ay8An/vGrONmtl7L5y78kGffncU7z6/n7CundvlY//jdEj7/rdm89OdlfPrWWSeUW6S/qQiJiMiA9OUrFvLnV+bw8mMre/X/qov0hYqiSpa/v4sLrz2FsPCwHpeqvtJQ08C0SRVEhHvYsGtEl24k62n3MD5vLzFRbawpGK2bz8qAo+mzRUQk4B0+K9db/8rnTy+fyTc/u1AlSAaEjKHpXHLDTCKiIggLDwuoEgSQkJLAD79dwua9o3jjn6u6tM1/ns5n+748vv/tKpUgCTr6L1pERBzT1tzGrZctJD2ximGZpcx/eS3guzD7iptOYuLwAn72yExnQ4oEkau/MpOh6fv49f1dK2l/eiyMwamlfPoW/T2U4KMiJCIijqgpq+WSMzbwl9fO5KLTtxAf08K5n5nId26cz0VX5jAopYq3308kLjHO6agiQSMyOpJv37KDhRum8MFLa465buHGIt5aOY1brtpGRFREPyUU6T8qQiIi/aCjrYNV72/h1ceXs7+w/OCyom0lFKzZzX+eWsGOtXu6tC/rtUe9c3yzu7nXMvelwo1FnDLZzfy1k3jivkX8/c05rFiXzmVnrOTXT80jIaaJd98NJ3tEltNRRYLOl75/KkNSS/nhj8OP+m+J9VruvqOI8DAPN39jTD8nFOkfKkIiIn3Iei2//94Chg2qZPo547j8llPJHpFJdkoZSYmtDDtpMGNOyeWTN85k9NThzJm4nref+fi5+xs/LODzFy7m0tnLOeeU1WSmVJE3aB8f/ncD1mupKavl/26Yz6CUcuISYxmcup/v3DifmrLaI/J4PV727SilrqKu317/ni3F7NlSjNfjBXwXX19/bTU1DYks+u92vvAd30xwiWmJvPD+LJ5/ZCmLFnh1fyCRPhITH8MP7ihgyabJPPfHpUc872n38MB3FvDc+6dzz1eXMHTsYAdSivQ9zRonItJHrNfywy8t4N7H53HO1NXcfGMLw0YksOSDarYXhBMX52XiREN0dBgjxrpYvqiGh54axd6KHH5++3y+9+BcVr2/hQs/nUWHJ4K8QaXExbQxang9i9cMZdf+4YSHdRy8l8iV85YyeUIr6zZG8/LCU0mOq+N7X17H134yi+jYaP70s0Xc98cR7K3wFYxPnr6c3/whg7HTRvTZ67/63GW88MFpACTEuDl/5iYamiJ4O386T/1mMdffOadPji0ix9bR1sHsyVvZWZLNpvVesnIzsF7Lf/++krt/nMLGPaO5aOZK/r34FMIjw52OK9Jjmj5bRMQBB+4Af8unFvKnl+Z0acaltuY2br58Bf94aw5ZyeWU16UzOKWM+e+1MWrK8IPrVZfW8PQf1lNebklOhnMuzmLa2eMOPr9u0Ta+e2c9b66YwZDUUk4aXsp7a07hzElrufLSOvbvtzzy7BQ83jCe+v1mLr+l9+8N8sR9i7j57jP42lULmDTJsHo1vD5/DJERHdx05Q5+9HDP7nAvIr1j8/IdTJ0zlEtOX8Nfnh3Lbddt5YUPTmNU9m7u/X4JV942SzPFyYCnIiQi0o9qy+v49d1r+OUT87jq7A955u3u/TLh9Xh59uGlvPa6YczINr55z8mkZqf0KMv7L67htw+0s7lwEFddtItfPH7mwSzFBaVcfnEN+QXjOWvKGkYNd1NSFsOEsU383y8mkzY4tUfHBKgqqWbk6HCmjCrk/dWT9cuUSIC675vzufvBeaTE1+BuSeCery7hrl/PDripv0V6SkVIRKQP1ZbX8eqTG9hR0MG/38tmfeFYAG68eBGPPjeDmPgYhxMeXVtzG7//4Yf85Znh1DUmkJFUy+aikaS7qnj1X/s5/eJJPdrvnZ+fz2//eSbrF+9k4umjezm1iPSWjrYOzp62ier6OJ5+soNTzhp3/I1EBhAVIRGRPmC9lpf+vIzbvzeC/bVZGONlzoQNnDe3hvM/mc6pF0x0OmKPrFu0jSuvjqaochDzX9/BrAu79zo2fljA9HlDuea8fJ78r64BEgl0nnYPYeFhOlVVgpKKkIhIL9u+upDbv1TF2/nTmTJiK4881M7UuaMDevSnOyqLq5g5vZEOTzhr1sV2+TS5kp37Oe20Dto7IshfCYNHDurjpCIiIkd3rCKku2OJiByDp93Dkv9tpNHdRnJqDEWFbl540fDqounERaXy++8t4Cs/nh10NxtMz0njhX+Vc/oFI/j85ev5z4fJx73O54OX1vC5WwdR15jMgv8VMXikTrEREZHAFVw/uUVEetGi19fxla/HsXHPyR9bnp5Yye1XL+H/fj6eQXlzHUrX96adPY4Hf7CQr/zkTL5/y3x+8bhvlrf6qnpWvr+D/KX15K+JZtPOTFrbI9i1fyp5WXv58N0SJs9RCRIRkcCmIiQichjrtfz86wv4ySNnMDxzH3//7RJGjk2kprKF5LQYZp43joioeU7H7Be3/fAMVq9eyH1/m8fm7cuZMrGZB56cTmPrKQCMGLSHyaNKiYy03HHTbr74nRnEJcY5nFpEROT4VIREJKR1tHXwjweXUVzUwfC8CLKyY3n0kXZeXTyPz12wmEf+dTKu1GFOx3SMCTM89soZjPz2fH7x51N4fUkinzh1JXfcEcb0eSNIzR4ODD/ebkRERAKOJksQkZC15D/r+erXo1nnn+76gJjIZu65fTl3/UY3/DxUe2s7xQX7yR2fo/dFREQGhGNNlqA73IlIyCnfW8H1Fy1mzicnU+1O4IVHltLc0MKmZTt44x/5lO/v4P8emKdf9g8TGR1J3sShel9ERCQo6NQ4EQkpze5mzptXzdaiGXzv5vl874EZxCcNAWD8qaMYf6qz+URERKR/qAiJSMjoaOvg1s+sYn3hHP7395Vc9Ll5TkcSERERh+jUOBEJCSvf2cw50zfyj7fm8NOvzueiz81wOpKIiIg4SCNCIhK0rNfyxj/y+c1vI5i/bipJcXU8/cBiPv+teU5HExEREYepCIlIUKosruLic0tZsW0GOWklPPB/87nlO9Nwpc5xOpqIiIgEABUhEQk6NWW1nDe3gq1FI3nivkV87huziIwe7HQsERERCSAqQiISVOoq6rjgzH1s3juK15/awAWfPcPpSCIiIhKAVIREJKB42j0sfH09Hy6ow+2GL3xtGGOnjTj4fG15HS8+vp7t2zy0txuGDLGc+8lsOto8bFxdzT0PDKe4agwv/2UNF3x2poOvRERERAKZipCI9Buvx0tLYwuxCbEfuylnR1sHC15bzwvPNPDK++Mor5sKQER4O79+Kpwr5i7l7HntrFxpePadaTS3nUF0ZAvREa3UNyfB/R8dY/TgQha8tpXTL1YJEhERkaNTERKRE9ba1Mqi/2yiuqKFuRePJis342PPVxRV8p9/beGnv81ld/lQjPGSFFfHrAkFREd5+XD9CCrqTyEuupFLZq/jM1fu4IKrJ9Lc0MLvf7qJPz4zlRfnJxEX3cjnLsrntjsymDr3JExYDEXbSlj0ZiHxCRGMGp/G2Gm5RETpnzYRERE5NmOtdTpDj0yfPt3m5+c7HUMkZHg9XnZvLiYyKoKwcENbSwfL3i/ivgfT2LRnJB7vR+VjUu52cgfXYAwU7kthw+4xAEwZsZXPXLKf5mYorwhjyepswsIsE0dVcOVnwrnwmsnEJcYdceyWxhZqy+tJSncR64rtt9csIiIiA5sxZpW1dnqnz6kIiQS39tZ21i0qoLSogcaGDhrcHpoavcTGGbZu8bJ8bSr7KlJITmjkji9VcuP/+aaX9rR72L+nnL88sI0FS5PYWDiUyvr0I/Y/OW8bnzynlFlz4sjMjuf9NyqYvySBsqoErDUMSncz9/QGzrownZnnjScsXPdxFhERkf6hIiQSgvLf3cxdd7ayfMtYmtuOHGUBiI5sYcbYbQwf3MDWwhRWFYxnaPo+3M3x1DUlYq2vtJx60kbGjazh9NM++vciMjKMjEHRXHjtKYRHhvfLaxIRERHpjmMVIZ1ILxKE9mwp5uIrM4gI7+BLV6zktNmRjBibTHxiNAlJscTGR9PU0EL64BTiEk8GfCNAv717Pus2RJKS3EFqiiUlxXDB5TmMmznR4VckIiIi0rtUhEQGsLbmNtYuKuB/r1SwZkMsCXEdREZaXvlgEsbAgnfcnDRjbpf2FR4Zzl33z+vbwCIiIiIBQkVIZIDxtHt44U/L+eNj8azcNpbW9gkY4+WknEJa2yNwN8Vx3szN/PRXGZw0Y5TTcUVEREQCkoqQSD+zXou1FmPMx+6lc0B7azu7NhZRvKuWpQvrKSoOIy3VS2sruBvCePvDEeypOJ2Tcnby1auWcdrsaM64aBRZuSMP2UvGEfsVERERkY+oCIn0MU+7h22rC1m1ZD+LFnp58d1J1DSmAL4bhibGukmKbyApvomWtkh2lA6jwzPi4PbpiZXUNCQTE9lCXHQzJ4/ay/33FPHpW08lLHzk0Q4rIiIiIsegIiTSy7weL4teX8+rL9Sxcn0Ka3eMoLF1FDCK+OgGPnXmOsaMasdaaGuDerehrj6c2voooiK9fPqCIsaeFMGwEQlMmJFDxtADU1Yn+P9otEdERETkRKkIifSSDUu288/HS/jXf0ZTVDmF2KgmThm9g5svW8W06eFMmz2Ik6bnER452+moIiIiIiFPRUjEr66ijuIdFaQNSiRreEan1++A7xqfkl37aWvpoKKknvlvVfDPV7JZXziW8LARXDhzNb/64R4uveFk4pMm9/OrEBEREZGuUBGSoFdRVMmLT2xmX7GXsvIw9lfEUOuOpr0j/OCf/dUpVNRnAEkAJMfXcsqYXZwysZ4JEyOoKO9gx84wdu5JYFPhEPbXZn/sGLNO2sBDP1jAVbeMJ3PYTAdepYiIiIh0h7HWHn+tADR9+nSbn5/vdAwJEF6Pl/K9lezZXs7eXW727W3DGNheAP/47xTqm5MIMx4ykyoZlFpDSmIzUZEeIiO8RIR7SU9tY/QoL0OHR1FZ3s7GTYbVm9JZXziSto5owDdpwcjB+xmTW8vM6R4SXOGkpkcyaeZg8iYOdfgdEBEREZHDGWNWWWund/acRoSkT3W0dVBTVktsQgzxSfFHnG7maffQWN9IW3M77W0dtLd10Nbie2xubKNyfxNR0eGUFDWys6AdjweGDguntsbDilVRrNg4lKLKbDzeCCDT/+cjCTFuLjh1Iz++N43xM0cSHpkFZHU5f3trO4WbCskamkpSRjqQftxtRERERCTwBUwRMsZcCPweCAcet9be53Ak8aspq6Vwcym7ttZSuLOVwt1hFBbFsq88icaWaJpaYvDaMMLDPISHeX1/wj00NMdRUZ+Gtb7yYIwXV0w9rtgmYqNbqW1MoNqdgtcm9ijXkNRS5kwp5NrcnURHQ3qGYVhuLMNHJzNkRDrWa0nOTCIi6rQev/bI6EjGnJLX4+1FREREJDAFRBEyxoQDfwTOA4qBlcaY1621m51NFpzaW9tpbmimtamNxvoWqsrcVJU1UV3ZSum+NvbuNewpjqZwXzKF+7Opa0oGkg9unxJfQ172fkbk1OCKbyc2xktYGHg84PEYPF6Dx2OIi/WQPchLerqhpcXidoPbbah3h9HUHE5SYgcZ6V6SkiAqyhAVbYiMPPAYRnRMGBmD4mhr9ZCaGcf4GXmEhYexd1sJSWkJpA3OBrKP8ipFRERERI4uIIoQMBPYYa3dBWCMeRb4FKAihO/0sYbaBty1TbhrmnDXtuCua8Vd1467vh13nQe3+6Oi4W4Iw90YgbsxEndjFO6mGNxNsbib43A3J/iveYk86vHioxsYlllG3uBqZk8rIy/XkjcqhhEnpZA3bhBJGSlASr+9/sONmDTMsWOLiIiISHAIlCI0BCg65Pti4NRjbbBlYwvTx/h6krXG/8hh3/sfOcryA+tzlOUHt+/p/nv+fFtHJG0dkbS2R9PuicI3m1nSsd4S/6lnblyxTbjimnDFteCKbyUjtQZXfDmuBK/vjwtiYyEm1hAXF0ZaRhSpGbGkZcWTNTSVlKxkTNhIYOQxjyciIiIiMlAFShHq7IYtR0xnZ4y5FbgVICF2LFmpjRhj/c8dWOfw7w8c4CjLj7b+geV0c/0jlh9vf51vHxlpiY6yREdDdDS4XOBKDMOVGIErKQJXYhSu5GhcybG4kuNwpcYT54rDhB2/MImIiIiIhLpAKULFwKHzD+cAJYevZK19DHgMfNNn/3fZjP5JJyIiIiIiQSXM6QB+K4HRxpg8Y0wUcA3wusOZREREREQkSAXEiJC1tsMY8zXgLXzTZz9hrd3kcCwREREREQlSAVGEAKy1/wP+53QOEREREREJfoFyapyIiIiIiEi/URESEREREZGQoyIkIiIiIiIhR0VIRERERERCjoqQiIiIiIiEHBUhEREREREJOSpCIiIiIiISclSEREREREQk5KgIiYiIiIhIyFEREhERERGRkKMiJCIiIiIiIUdFSEREREREQo6KkIiIiIiIhBwVIRERERERCTkqQiIiIiIiEnJUhEREREREJOSoCImIiIiISMgx1lqnM/SIMcYN7AfqnM5ymCQCL9MB6UCl0yE6EcjvWaBmC9TPEgL3PTsgUPPpM+2+QM0F+jy7KpCydCYQP8dAf88CNV8gfpYHBOp7BoGbrTuf51hrrauzJyJ6L0+/2wasttbe6nSQQxljHgu0TAcYY/KttdOdznG4AH/PAjJboH6WELjv2QGBmk+fafcFai7Q59lVgZSlM4H4OQ6A9ywg8wXiZ3lAoL5nELjZuvN5GmPyj/bcQD817t9OB+hEIGYKdIH8ngVytkAV6O9ZoOcLRIH6ngVqrkAXSO9bIGUZKAL9PQv0fIEokN+zQM52wgbyqXEB2+wDld6z4KHPMvjoMw0u+jyDgz7H4KHPMrh0d0ToaOsO5BGhx5wOMADpPQse+iyDjz7T4KLPMzjocwwe+iyDS3c+z6OuO2BHhERERERERHpqII8IiYiIiIiI9IiKUBAyxlxujLHGmJOcziK9wxjTcJzn5xtjdO5zADPG5BhjXjPGFBhjdhpjfm+MiTrG+t8wxsT1Z0bpvuP93ZTAp5+ZwUc/M4NDf/zcVBEKTtcCi4FrurORMSa8b+KIhDZjjAFeBl611o4GxgAJwL3H2OwbgIqQSN/Tz0yRANNfPzdVhIKMMSYBmA3cjP8fdWPMPGPMQmPMK8aYzcaYPxljwvzPNRhjfmqMWQ6c5lxyOR7/5/ifQ75/2Bhzo4ORpOvOBlqstX8DsNZ6gG8CNxlj4o0x9xtjNhhj1htjbjfGfB0YDHxgjPnAwdzSBcaYBGPMe8aY1f7P8VP+5bnGmC3GmL8YYzYZY942xsQ6nVc+op+ZwUs/Mwe8fvm5OZBvqCqduwx401q73RhTbYw5xb98JjAe2AO8CXwaeBGIBzZaa3/kRFiREDEBWHXoAmttvTFmL/BFIA+Yaq3tMMakWmurjTHfAs6y1gbqndDlIy3A5f7PNB1YZox53f/caOBaa+0txpjngSuAfzgVVI5wGfqZKRKI+uXnpkaEgs+1wLP+r5/1fw+wwlq7y9+onwHm+Jd7gJf6N6JIyDFAZ1N0GuBM4E/W2g4Aa211fwaTXmGAXxhj1gPvAkOALP9zhdbatf6vVwG5/Z5OjkU/M0UCU7/83NSIUBAxxqThG0qcaIyxQDi+/4j+x5H/MR34vsX/D70Evg4+/j8vYpwKIt22Cd9IwEHGmERgKLCLzv+xl4HjOiADmGatbTfG7Oajv5+th6znAXRqXIDQz8ygp5+ZA1u//NzUiFBwuRJ42lo73Fqba60dChTi+z9ZM40xef7znK/Gd2GoDCx7gPHGmGhjTBJwjtOBpMveA+KMMdfDwYusHwCeBN4GbjPGRPifS/Vv4wZc/R9VeiAJKPeXoLOA4U4Hki7Rz8zgpp+ZA1u//NxUEQou1wKvHLbsJeCzwFLgPmAjvn/oD19PApT/L3qrtbYIeB5YD/wTWONoMOky67tz9eXAZ4wxBcB2fNeVfA94HNgLrDfGrMP39xV8d8J+Q5MlBK4Dfzfx/X2cbozJxzc6tNXRYNJV+pkZhPQzMzj0189N4zuOBDNjzDzgTmvtJQ5HkR4wxpwM/MVaO9PpLCLyEf3dDE76mTmw6e+ldIdGhEQCmDHmNnwX6v7A6Swi8hH93RQJPPp7Kd2lESEREREREQk5GhESEREREZGQoyIkIiJyHMaYocaYD4wxW4wxm4wxd/iXpxpj3jHGFPgfU/zLzzPGrPLf+XyVMebsQ/Z1rzGmyBjT4NTrERERnRonIiJyXMaYbCDbWrvaGOPCd3PUy4AbgWpr7X3GmO8CKdba7xhjpgJl1toSY8xE4C1r7RD/vmbhm9q3wFqb4MTrERERFSEREZFuM8a8Bjzs/zPPWlvqL0vzrbVjD1vXAJXAYGtt6yHLG1SERESco1PjREREusEYkwtMBZYDWdbaUgD/Y2Ynm1wBrDm0BImIiPMinA4gIiIyUBhjEvDddPMb1tp632DPMdefAPwKOL8f4omISDdoREhERKQLjDGR+ErQP621L/sXl/lPiTtwHVH5IevnAK8A11trd/Z3XhEROTYVIRERkePwX+fzV2CLtfa3hzz1OnCD/+sbgNf86ycD/wXuttYu6ceoIiLSRZosQURE5DiMMXOARcAGwOtf/D181wk9DwwD9gKfsdZWG2N+ANwNFByym/OtteXGmF8DnwUGAyXA49ban/TLCxERkYNUhEREREREJOTo1DgREREREQk5KkIiIiIiIhJyVIRERERERCTkqAiJiIiIiEjIURESEREREZGQoyIkIiIiIiIhR0VIRERERERCjoqQiIiIiIiEnP8HIfCFTB9oKlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ma=data.cases_daily.rolling(window=10).mean().dropna()\n",
    "df=forecasting_process(df_ma,0.7,7,60)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit ('base': conda)",
   "language": "python",
   "name": "python395jvsc74a57bd059c0d86d9edace904dfbc9e68f1aa96dccb007356870c6175f521ee10fc158bb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}