Forecast_with_ARIMA/Forecating_with_ARIMA_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f1fcce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9f1e219",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./wind_turbine_20220114.csv\", encoding=\"latin1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2325311f",
   "metadata": {},
   "source": [
    "**PART-1:** EXPLORATORY DATA ANALYSES\n",
    "\n",
    "KEY QUESTIONS:\n",
    "1. How many columns are there?\n",
    "2. What are the columns headers?\n",
    "3. How the dataframe is arrage?\n",
    "4. What data types are those columns?\n",
    "5. Which columns nulls?\n",
    "\n",
    "KEY OUTCOMES:\n",
    "1. No nulls in my columns of interest, in this case project year.\n",
    "2. Prepare a new dataset with a column \"cumulative_number_of_windturbine)\n",
    "3. Data ready for for a forecast modeling using ARIMA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3edf59f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70808, 27)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape  #KQ- 1|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f9185a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['case_id', 'faa_ors', 'faa_asn', 'usgs_pr_id', 'eia_id', 't_state',\n",
       "       't_county', 't_fips', 'p_name', 'p_year', 'p_tnum', 'p_cap', 't_manu',\n",
       "       't_model', 't_cap', 't_hh', 't_rd', 't_rsa', 't_ttlh', 'retrofit',\n",
       "       'retrofit_year', 't_conf_atr', 't_conf_loc', 't_img_date', 't_img_srce',\n",
       "       'xlong', 'ylat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns #KQ- 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c2b657",
   "metadata": {},
   "source": [
    "The column names in this dataset too much abbreviated. If you want you can change all the column name to something that makes more sence to you but for now I am only going to chanege the column name for p_column and p_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "811569c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={\"p_year\": \"project_year\", \"p_name\": \"project_name\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a13cf31a",
   "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></th>\n",
       "      <th>case_id</th>\n",
       "      <th>faa_ors</th>\n",
       "      <th>faa_asn</th>\n",
       "      <th>usgs_pr_id</th>\n",
       "      <th>eia_id</th>\n",
       "      <th>t_state</th>\n",
       "      <th>t_county</th>\n",
       "      <th>t_fips</th>\n",
       "      <th>project_name</th>\n",
       "      <th>project_year</th>\n",
       "      <th>...</th>\n",
       "      <th>t_rsa</th>\n",
       "      <th>t_ttlh</th>\n",
       "      <th>retrofit</th>\n",
       "      <th>retrofit_year</th>\n",
       "      <th>t_conf_atr</th>\n",
       "      <th>t_conf_loc</th>\n",
       "      <th>t_img_date</th>\n",
       "      <th>t_img_srce</th>\n",
       "      <th>xlong</th>\n",
       "      <th>ylat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3072661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5149.0</td>\n",
       "      <td>52161.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>6029</td>\n",
       "      <td>251 Wind</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5/8/2018</td>\n",
       "      <td>Digital Globe</td>\n",
       "      <td>-118.363762</td>\n",
       "      <td>35.077908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3072695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5143.0</td>\n",
       "      <td>52161.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>6029</td>\n",
       "      <td>251 Wind</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5/8/2018</td>\n",
       "      <td>Digital Globe</td>\n",
       "      <td>-118.364410</td>\n",
       "      <td>35.077435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3072704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5146.0</td>\n",
       "      <td>52161.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>6029</td>\n",
       "      <td>251 Wind</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5/8/2018</td>\n",
       "      <td>Digital Globe</td>\n",
       "      <td>-118.364197</td>\n",
       "      <td>35.077644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3063272</td>\n",
       "      <td>19-028134</td>\n",
       "      <td>2014-WTE-4084-OE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IA</td>\n",
       "      <td>Story County</td>\n",
       "      <td>19169</td>\n",
       "      <td>30 MW Iowa DG Portfolio</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12271.85</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4/24/2017</td>\n",
       "      <td>Digital Globe</td>\n",
       "      <td>-93.430367</td>\n",
       "      <td>42.028233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3053390</td>\n",
       "      <td>19-028015</td>\n",
       "      <td>2015-WTE-6386-OE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IA</td>\n",
       "      <td>Boone County</td>\n",
       "      <td>19015</td>\n",
       "      <td>30 MW Iowa DG Portfolio</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12271.85</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6/1/2017</td>\n",
       "      <td>Digital Globe</td>\n",
       "      <td>-93.700424</td>\n",
       "      <td>41.977608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id    faa_ors           faa_asn  usgs_pr_id   eia_id t_state  \\\n",
       "0  3072661        NaN               NaN      5149.0  52161.0      CA   \n",
       "1  3072695        NaN               NaN      5143.0  52161.0      CA   \n",
       "2  3072704        NaN               NaN      5146.0  52161.0      CA   \n",
       "3  3063272  19-028134  2014-WTE-4084-OE         NaN      NaN      IA   \n",
       "4  3053390  19-028015  2015-WTE-6386-OE         NaN      NaN      IA   \n",
       "\n",
       "       t_county  t_fips             project_name  project_year  ...     t_rsa  \\\n",
       "0   Kern County    6029                 251 Wind        1987.0  ...       NaN   \n",
       "1   Kern County    6029                 251 Wind        1987.0  ...       NaN   \n",
       "2   Kern County    6029                 251 Wind        1987.0  ...       NaN   \n",
       "3  Story County   19169  30 MW Iowa DG Portfolio        2017.0  ...  12271.85   \n",
       "4  Boone County   19015  30 MW Iowa DG Portfolio        2017.0  ...  12271.85   \n",
       "\n",
       "   t_ttlh retrofit retrofit_year  t_conf_atr  t_conf_loc  t_img_date  \\\n",
       "0     NaN        0           NaN           2           3    5/8/2018   \n",
       "1     NaN        0           NaN           2           3    5/8/2018   \n",
       "2     NaN        0           NaN           2           3    5/8/2018   \n",
       "3   150.0        0           NaN           3           3   4/24/2017   \n",
       "4   150.0        0           NaN           3           3    6/1/2017   \n",
       "\n",
       "      t_img_srce       xlong       ylat  \n",
       "0  Digital Globe -118.363762  35.077908  \n",
       "1  Digital Globe -118.364410  35.077435  \n",
       "2  Digital Globe -118.364197  35.077644  \n",
       "3  Digital Globe  -93.430367  42.028233  \n",
       "4  Digital Globe  -93.700424  41.977608  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #KQ- 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dcf378c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 70808 entries, 0 to 70807\n",
      "Data columns (total 27 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   case_id        70808 non-null  int64  \n",
      " 1   faa_ors        65016 non-null  object \n",
      " 2   faa_asn        65686 non-null  object \n",
      " 3   usgs_pr_id     38263 non-null  float64\n",
      " 4   eia_id         65015 non-null  float64\n",
      " 5   t_state        70808 non-null  object \n",
      " 6   t_county       70808 non-null  object \n",
      " 7   t_fips         70808 non-null  int64  \n",
      " 8   project_name   70808 non-null  object \n",
      " 9   project_year   70195 non-null  float64\n",
      " 10  p_tnum         70808 non-null  int64  \n",
      " 11  p_cap          66326 non-null  float64\n",
      " 12  t_manu         65168 non-null  object \n",
      " 13  t_model        65031 non-null  object \n",
      " 14  t_cap          65328 non-null  float64\n",
      " 15  t_hh           64628 non-null  float64\n",
      " 16  t_rd           64874 non-null  float64\n",
      " 17  t_rsa          64874 non-null  float64\n",
      " 18  t_ttlh         64628 non-null  float64\n",
      " 19  retrofit       70808 non-null  int64  \n",
      " 20  retrofit_year  5986 non-null   float64\n",
      " 21  t_conf_atr     70808 non-null  int64  \n",
      " 22  t_conf_loc     70808 non-null  int64  \n",
      " 23  t_img_date     62492 non-null  object \n",
      " 24  t_img_srce     70808 non-null  object \n",
      " 25  xlong          70808 non-null  float64\n",
      " 26  ylat           70808 non-null  float64\n",
      "dtypes: float64(12), int64(6), object(9)\n",
      "memory usage: 15.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info() #KQ- 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0649ddf7",
   "metadata": {},
   "source": [
    "# Lets deal with the null value.\n",
    "First let's see which columns have most null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18327ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull());   #KQ- 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f554c7a",
   "metadata": {},
   "source": [
    "Looks like there are some null value in project_year column, however their number look very few compared to the rest of dataset. So I am just going to delete those rows where project_year is null.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d5d91ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(subset=[\"project_year\"], inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1378856d",
   "metadata": {},
   "source": [
    "Lets take a look at the project_year column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d550718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1987.0\n",
       "1        1987.0\n",
       "2        1987.0\n",
       "3        2017.0\n",
       "4        2017.0\n",
       "          ...  \n",
       "70802    2021.0\n",
       "70803    2016.0\n",
       "70805    2011.0\n",
       "70806    2016.0\n",
       "70807    2016.0\n",
       "Name: project_year, Length: 70195, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"project_year\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ba9205",
   "metadata": {},
   "source": [
    "These years are float64 datatype. So let's convert them to a datetime object and have a yyyy-mm-dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01358b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.to_datetime(data[\"project_year\"])\n",
    "data[\"project_year\"] = pd.to_datetime(data[\"project_year\"], format=\"%Y\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a494da",
   "metadata": {},
   "source": [
    "Now let's see how the project_year column looks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74bc457e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1987-01-01\n",
       "1       1987-01-01\n",
       "2       1987-01-01\n",
       "3       2017-01-01\n",
       "4       2017-01-01\n",
       "           ...    \n",
       "70802   2021-01-01\n",
       "70803   2016-01-01\n",
       "70805   2011-01-01\n",
       "70806   2016-01-01\n",
       "70807   2016-01-01\n",
       "Name: project_year, Length: 70195, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"project_year\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e348f8e6",
   "metadata": {},
   "source": [
    "So now the data type is datetime64[ns] and the format looks exactly what I wanted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96060647",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the following code prints the dataframe. but this dataframe is way too big to be printed so don't print this.\n",
    "# print(data.to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a16365",
   "metadata": {},
   "source": [
    "Now lets make a plot. I want to see a plot that shows the number of Wind Turbine added each year for the years in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8eb2447e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(\"project_year\")[\"case_id\"].count().plot()\n",
    "plt.title(\"Annual Wind Capacity\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5eec7f7",
   "metadata": {},
   "source": [
    "Now lets create a table where we will sum up the numbers of wind turbines that were established in the each year since 1982 and name the columns as \"year\" and \"count\".\n",
    "We will use value_counts to aggregate the turbine number, then use sort values and finally reset_index in case there is a data missing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2b434bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_in_years = data['project_year'].value_counts().sort_index().reset_index()\n",
    "counts_in_years.columns = ['year', 'count']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1f9404",
   "metadata": {},
   "source": [
    "Now let's make it a pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b25fc9ad",
   "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></th>\n",
       "      <th>year</th>\n",
       "      <th>count</th>\n",
       "      <th>cumulative_windturbine_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1982-01-01</td>\n",
       "      <td>930</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983-01-01</td>\n",
       "      <td>433</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984-01-01</td>\n",
       "      <td>155</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>516</td>\n",
       "      <td>2044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>79</td>\n",
       "      <td>2123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>193</td>\n",
       "      <td>2316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>162</td>\n",
       "      <td>2478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>288</td>\n",
       "      <td>2766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1991-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>2770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>10</td>\n",
       "      <td>2780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>21</td>\n",
       "      <td>2801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>14</td>\n",
       "      <td>2815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>230</td>\n",
       "      <td>3045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>186</td>\n",
       "      <td>3231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>618</td>\n",
       "      <td>3849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>72</td>\n",
       "      <td>3921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>1870</td>\n",
       "      <td>5791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>461</td>\n",
       "      <td>6252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>1152</td>\n",
       "      <td>7404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>268</td>\n",
       "      <td>7672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>1638</td>\n",
       "      <td>9310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>1418</td>\n",
       "      <td>10728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>3200</td>\n",
       "      <td>13928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>5047</td>\n",
       "      <td>18975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>5701</td>\n",
       "      <td>24676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2962</td>\n",
       "      <td>27638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>3497</td>\n",
       "      <td>31135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>6768</td>\n",
       "      <td>37903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>600</td>\n",
       "      <td>38503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2506</td>\n",
       "      <td>41009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>4301</td>\n",
       "      <td>45310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>3798</td>\n",
       "      <td>49108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>3058</td>\n",
       "      <td>52166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3136</td>\n",
       "      <td>55302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>3588</td>\n",
       "      <td>58890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>6592</td>\n",
       "      <td>65482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>4713</td>\n",
       "      <td>70195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  count  cumulative_windturbine_num\n",
       "0  1981-01-01     10                          10\n",
       "1  1982-01-01    930                         940\n",
       "2  1983-01-01    433                        1373\n",
       "3  1984-01-01    155                        1528\n",
       "4  1985-01-01    516                        2044\n",
       "5  1986-01-01     79                        2123\n",
       "6  1987-01-01    193                        2316\n",
       "7  1988-01-01    162                        2478\n",
       "8  1989-01-01    288                        2766\n",
       "9  1990-01-01      1                        2767\n",
       "10 1991-01-01      1                        2768\n",
       "11 1992-01-01      2                        2770\n",
       "12 1994-01-01     10                        2780\n",
       "13 1995-01-01     21                        2801\n",
       "14 1996-01-01     14                        2815\n",
       "15 1997-01-01    230                        3045\n",
       "16 1998-01-01    186                        3231\n",
       "17 1999-01-01    618                        3849\n",
       "18 2000-01-01     72                        3921\n",
       "19 2001-01-01   1870                        5791\n",
       "20 2002-01-01    461                        6252\n",
       "21 2003-01-01   1152                        7404\n",
       "22 2004-01-01    268                        7672\n",
       "23 2005-01-01   1638                        9310\n",
       "24 2006-01-01   1418                       10728\n",
       "25 2007-01-01   3200                       13928\n",
       "26 2008-01-01   5047                       18975\n",
       "27 2009-01-01   5701                       24676\n",
       "28 2010-01-01   2962                       27638\n",
       "29 2011-01-01   3497                       31135\n",
       "30 2012-01-01   6768                       37903\n",
       "31 2013-01-01    600                       38503\n",
       "32 2014-01-01   2506                       41009\n",
       "33 2015-01-01   4301                       45310\n",
       "34 2016-01-01   3798                       49108\n",
       "35 2017-01-01   3058                       52166\n",
       "36 2018-01-01   3136                       55302\n",
       "37 2019-01-01   3588                       58890\n",
       "38 2020-01-01   6592                       65482\n",
       "39 2021-01-01   4713                       70195"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(counts_in_years)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfba387",
   "metadata": {},
   "source": [
    "# To forecast model of the cumulative number of the wind turbine lets create a cumulative column using cumsum() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02c4aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cumulative_windturbine_num\"] = df[\"count\"].cumsum()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4734647",
   "metadata": {},
   "source": [
    "# Now lets create a predictive model using ARIMA model\n",
    "\n",
    "\n",
    "\n",
    "To create a predictive model we need a continuous dataset with seasonality. let's create a column with the cumulative number of wind turbine and:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe90c63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets import adfller to test if the data is stationary or not\n",
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1252d20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.39115576334690016,\n",
       " 0.9115650083381044,\n",
       " 2,\n",
       " 37,\n",
       " {'1%': -3.6209175221605827,\n",
       "  '5%': -2.9435394610388332,\n",
       "  '10%': -2.6104002410518627},\n",
       " 511.654591295898)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf_results = adfuller(df[\"count\"])\n",
    "adf_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eb04dd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.39115576334690016"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_stat = adf_results[0]\n",
    "test_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8be8b22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9115650083381044"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value = adf_results[1]\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cdd52bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1%': -3.6209175221605827,\n",
       " '5%': -2.9435394610388332,\n",
       " '10%': -2.6104002410518627}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_values = adf_results[4]\n",
    "critical_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d7457ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cumulative_windturbine_num\"] = df[\"count\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "49b4c840",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>cumulative_windturbine_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>930</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>433</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>155</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>516</td>\n",
       "      <td>2044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>79</td>\n",
       "      <td>2123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>193</td>\n",
       "      <td>2316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>162</td>\n",
       "      <td>2478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>288</td>\n",
       "      <td>2766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>2770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>2780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>21</td>\n",
       "      <td>2801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>14</td>\n",
       "      <td>2815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>230</td>\n",
       "      <td>3045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>186</td>\n",
       "      <td>3231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>618</td>\n",
       "      <td>3849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>72</td>\n",
       "      <td>3921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1870</td>\n",
       "      <td>5791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>461</td>\n",
       "      <td>6252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1152</td>\n",
       "      <td>7404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>268</td>\n",
       "      <td>7672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>1638</td>\n",
       "      <td>9310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1418</td>\n",
       "      <td>10728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>3200</td>\n",
       "      <td>13928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>5047</td>\n",
       "      <td>18975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>5701</td>\n",
       "      <td>24676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>2962</td>\n",
       "      <td>27638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>3497</td>\n",
       "      <td>31135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>6768</td>\n",
       "      <td>37903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>600</td>\n",
       "      <td>38503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>2506</td>\n",
       "      <td>41009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4301</td>\n",
       "      <td>45310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3798</td>\n",
       "      <td>49108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>3058</td>\n",
       "      <td>52166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>3136</td>\n",
       "      <td>55302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3588</td>\n",
       "      <td>58890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>6592</td>\n",
       "      <td>65482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>4713</td>\n",
       "      <td>70195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  cumulative_windturbine_num\n",
       "year                                         \n",
       "1981-01-01     10                          10\n",
       "1982-01-01    930                         940\n",
       "1983-01-01    433                        1373\n",
       "1984-01-01    155                        1528\n",
       "1985-01-01    516                        2044\n",
       "1986-01-01     79                        2123\n",
       "1987-01-01    193                        2316\n",
       "1988-01-01    162                        2478\n",
       "1989-01-01    288                        2766\n",
       "1990-01-01      1                        2767\n",
       "1991-01-01      1                        2768\n",
       "1992-01-01      2                        2770\n",
       "1994-01-01     10                        2780\n",
       "1995-01-01     21                        2801\n",
       "1996-01-01     14                        2815\n",
       "1997-01-01    230                        3045\n",
       "1998-01-01    186                        3231\n",
       "1999-01-01    618                        3849\n",
       "2000-01-01     72                        3921\n",
       "2001-01-01   1870                        5791\n",
       "2002-01-01    461                        6252\n",
       "2003-01-01   1152                        7404\n",
       "2004-01-01    268                        7672\n",
       "2005-01-01   1638                        9310\n",
       "2006-01-01   1418                       10728\n",
       "2007-01-01   3200                       13928\n",
       "2008-01-01   5047                       18975\n",
       "2009-01-01   5701                       24676\n",
       "2010-01-01   2962                       27638\n",
       "2011-01-01   3497                       31135\n",
       "2012-01-01   6768                       37903\n",
       "2013-01-01    600                       38503\n",
       "2014-01-01   2506                       41009\n",
       "2015-01-01   4301                       45310\n",
       "2016-01-01   3798                       49108\n",
       "2017-01-01   3058                       52166\n",
       "2018-01-01   3136                       55302\n",
       "2019-01-01   3588                       58890\n",
       "2020-01-01   6592                       65482\n",
       "2021-01-01   4713                       70195"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37518b4",
   "metadata": {},
   "source": [
    "Everything looks great lets set the year column as index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "499177f4",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['year'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6n/c4n1zckn7vs1dh020lln_h6m0000gn/T/ipykernel_39126/75991961.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"year\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   5501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5502\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5503\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5505\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of ['year'] are in the columns\""
     ]
    }
   ],
   "source": [
    "df.set_index(\"year\", inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a9164553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x= df.index, y=\"cumulative_windturbine_num\", data=df)\n",
    "# sns.lineplot(x= df.index, y=\"count\", data=df)\n",
    "plt.title(\"Cumulative Wind Capacity\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99c91e7",
   "metadata": {},
   "source": [
    "**Now time for some modeling:**\n",
    "\n",
    "**Step 1:** import the from statsmodels.tsa.arima.model import ARIMA (it has been imported in the cell 1)\n",
    "**Step 2:** resample the data on a yearly frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b65e6015",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf_results = adfuller(df[\"cumulative_windturbine_num\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "969fdaa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9671504688306003"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_stat = adf_results[0]\n",
    "test_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6e539ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value = adf_results[1]\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "30febc0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1%': -3.6155091011809297,\n",
       " '5%': -2.941262357486514,\n",
       " '10%': -2.6091995013850418}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critical_values = adf_results[4]\n",
    "critical_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "06f8ca92",
   "metadata": {},
   "outputs": [],
   "source": [
    "growth_over_year = df.resample(\"Y\")[\"cumulative_windturbine_num\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05bcecb7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This function does not handle missing values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6n/c4n1zckn7vs1dh020lln_h6m0000gn/T/ipykernel_12033/3829599740.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdecomposition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseasonal_decompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrowth_over_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecomposition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/seasonal.py\u001b[0m in \u001b[0;36mseasonal_decompose\u001b[0;34m(x, model, filt, period, two_sided, extrapolate_trend)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This function does not handle missing values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"m\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: This function does not handle missing values"
     ]
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(growth_over_year)\n",
    "fig = plt.figure()\n",
    "fig = decomposition.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6363377",
   "metadata": {},
   "source": [
    "lets check if where is the missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "41d315e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1982-01-01', '1983-01-01', '1984-01-01', '1985-01-01',\n",
       "               '1986-01-01', '1987-01-01', '1988-01-01', '1989-01-01',\n",
       "               '1990-01-01', '1991-01-01', '1992-01-01', '1994-01-01',\n",
       "               '1995-01-01', '1996-01-01', '1997-01-01', '1998-01-01',\n",
       "               '1999-01-01', '2000-01-01', '2001-01-01', '2002-01-01',\n",
       "               '2003-01-01', '2004-01-01', '2005-01-01', '2006-01-01',\n",
       "               '2007-01-01', '2008-01-01', '2009-01-01', '2010-01-01',\n",
       "               '2011-01-01', '2012-01-01', '2013-01-01', '2014-01-01',\n",
       "               '2015-01-01', '2016-01-01', '2017-01-01', '2018-01-01',\n",
       "               '2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01'],\n",
       "              dtype='datetime64[ns]', name='year', freq=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a9de36",
   "metadata": {},
   "source": [
    "**Looking at the index it is clean that year 1993-01-01 is missing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c565cf7a",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>cumulative_windturbine_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>930</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>433</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>155</td>\n",
       "      <td>1518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>482</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>79</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>193</td>\n",
       "      <td>2272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>162</td>\n",
       "      <td>2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>288</td>\n",
       "      <td>2722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>2726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>2736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>21</td>\n",
       "      <td>2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>230</td>\n",
       "      <td>2988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>184</td>\n",
       "      <td>3172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>550</td>\n",
       "      <td>3722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>41</td>\n",
       "      <td>3763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1776</td>\n",
       "      <td>5539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>458</td>\n",
       "      <td>5997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1120</td>\n",
       "      <td>7117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>162</td>\n",
       "      <td>7279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>1240</td>\n",
       "      <td>8519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1023</td>\n",
       "      <td>9542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>2991</td>\n",
       "      <td>12533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>4849</td>\n",
       "      <td>17382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>5701</td>\n",
       "      <td>23083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>2958</td>\n",
       "      <td>26041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>3573</td>\n",
       "      <td>29614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>6643</td>\n",
       "      <td>36257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>599</td>\n",
       "      <td>36856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>2505</td>\n",
       "      <td>39361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4305</td>\n",
       "      <td>43666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3797</td>\n",
       "      <td>47463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4361</td>\n",
       "      <td>51824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>3116</td>\n",
       "      <td>54940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>3587</td>\n",
       "      <td>58527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>6444</td>\n",
       "      <td>64971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>4747</td>\n",
       "      <td>69718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>2395</td>\n",
       "      <td>72113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  cumulative_windturbine_num\n",
       "year                                         \n",
       "1982-01-01    930                         930\n",
       "1983-01-01    433                        1363\n",
       "1984-01-01    155                        1518\n",
       "1985-01-01    482                        2000\n",
       "1986-01-01     79                        2079\n",
       "1987-01-01    193                        2272\n",
       "1988-01-01    162                        2434\n",
       "1989-01-01    288                        2722\n",
       "1990-01-01      1                        2723\n",
       "1991-01-01      1                        2724\n",
       "1992-01-01      2                        2726\n",
       "1994-01-01     10                        2736\n",
       "1995-01-01     21                        2757\n",
       "1996-01-01      1                        2758\n",
       "1997-01-01    230                        2988\n",
       "1998-01-01    184                        3172\n",
       "1999-01-01    550                        3722\n",
       "2000-01-01     41                        3763\n",
       "2001-01-01   1776                        5539\n",
       "2002-01-01    458                        5997\n",
       "2003-01-01   1120                        7117\n",
       "2004-01-01    162                        7279\n",
       "2005-01-01   1240                        8519\n",
       "2006-01-01   1023                        9542\n",
       "2007-01-01   2991                       12533\n",
       "2008-01-01   4849                       17382\n",
       "2009-01-01   5701                       23083\n",
       "2010-01-01   2958                       26041\n",
       "2011-01-01   3573                       29614\n",
       "2012-01-01   6643                       36257\n",
       "2013-01-01    599                       36856\n",
       "2014-01-01   2505                       39361\n",
       "2015-01-01   4305                       43666\n",
       "2016-01-01   3797                       47463\n",
       "2017-01-01   4361                       51824\n",
       "2018-01-01   3116                       54940\n",
       "2019-01-01   3587                       58527\n",
       "2020-01-01   6444                       64971\n",
       "2021-01-01   4747                       69718\n",
       "2022-01-01   2395                       72113"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8477d293",
   "metadata": {},
   "source": [
    "Lets insert a row where count = 0 and cumulative_windturbine_num = 2726 since at the end ot 1992 the cumulative number wind turbine was 2726"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3cee8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_row = {'count': 0,\n",
    "           \"cumulative_windturbine_num\":2726}\n",
    "new_date = pd.to_datetime('1993-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3e7fa00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[new_date] = new_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6c330786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                            0\n",
       "cumulative_windturbine_num    2726\n",
       "Name: 1993-01-01 00:00:00, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1993-01-01\"] #just checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b938a4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "growth_over_year = df.resample(\"Y\")[\"cumulative_windturbine_num\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a1a0360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(growth_over_year)\n",
    "fig = plt.figure()\n",
    "fig = decomposition.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7bea3d",
   "metadata": {},
   "source": [
    "Lets divide this data set as train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a6eed93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = growth_over_year[\"1998-01-01\": \"2017-01-01\"].astype(float)\n",
    "test = growth_over_year[\"2017-01-01\" : \"2022-01-01\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cede2c0e",
   "metadata": {},
   "source": [
    "Let's check out how does the test and train data look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2da2c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (6,4))\n",
    "train.plot()\n",
    "test.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a23b340",
   "metadata": {},
   "source": [
    "Now we are finally ready to use ARIMA model to generate a prediction regarding the number of Wind Turbine by 2025. For this dataset I will use the following values for order=(1,1,1). These values are also known as p, d, q. Here the order=(1,1,1) means that we are using an ARIMA model with 1 autoregressive term, 1 differencing term, and 1 moving average term. When there is a apparent trend in the data the q = 1 (differencing term) is used.\n",
    "Since our data has apparent trend I am using order=(1,1,1). In case no apparent trend I would have used order=(1,0,1).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ff52f774",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chowdhury/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n"
     ]
    }
   ],
   "source": [
    "model = ARIMA(train, order=(1,1,1)).fit() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e9a799",
   "metadata": {},
   "source": [
    "Now let's do get some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4524b4a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chowdhury/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'The `start` argument could not be matched to a location related to the index of the data.'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 883612800000000000",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3628\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3629\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: Timestamp('1998-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    695\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    697\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: Timestamp('1998-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36mget_prediction_index\u001b[0;34m(start, end, nobs, base_index, index, silent, index_none, index_generated, data)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m         start, _, start_oos = get_index_label_loc(\n\u001b[0m\u001b[1;32m    357\u001b[0m             \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrow_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36mget_index_label_loc\u001b[0;34m(key, index, row_labels)\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_was_expanded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36mget_index_label_loc\u001b[0;34m(key, index, row_labels)\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_was_expanded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_index_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36mget_index_loc\u001b[0;34m(key, index)\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0;31m# (note that get_loc will throw a KeyError if key is invalid)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mint_index\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrange_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_key\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '1998-01-01'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/6n/c4n1zckn7vs1dh020lln_h6m0000gn/T/ipykernel_12033/1289027160.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"1998-01-01\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"2022-01-01\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/wrapper.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, start, end, dynamic, **kwargs)\u001b[0m\n\u001b[1;32m   3401\u001b[0m         \"\"\"\n\u001b[1;32m   3402\u001b[0m         \u001b[0;31m# Perform the prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3403\u001b[0;31m         \u001b[0mprediction_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3404\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mprediction_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py\u001b[0m in \u001b[0;36mget_prediction\u001b[0;34m(self, start, end, dynamic, index, exog, extend_model, extend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m   3285\u001b[0m         \u001b[0;31m# Handle start, end, dynamic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3286\u001b[0m         start, end, out_of_sample, prediction_index = (\n\u001b[0;32m-> 3287\u001b[0;31m             self.model._get_prediction_index(start, end, index))\n\u001b[0m\u001b[1;32m   3288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3289\u001b[0m         \u001b[0;31m# Handle `dynamic`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36m_get_prediction_index\u001b[0;34m(self, start, end, index, silent)\u001b[0m\n\u001b[1;32m    832\u001b[0m         \"\"\"\n\u001b[1;32m    833\u001b[0m         \u001b[0mnobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 834\u001b[0;31m         return get_prediction_index(\n\u001b[0m\u001b[1;32m    835\u001b[0m             \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m             \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36mget_prediction_index\u001b[0;34m(start, end, nobs, base_index, index, silent, index_none, index_generated, data)\u001b[0m\n\u001b[1;32m    358\u001b[0m         )\n\u001b[1;32m    359\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m         raise KeyError(\n\u001b[0m\u001b[1;32m    361\u001b[0m             \u001b[0;34m\"The `start` argument could not be matched to a\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0;34m\" location related to the index of the data.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'The `start` argument could not be matched to a location related to the index of the data.'"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(start=\"1998-01-01\", end=\"2022-01-01\")\n",
    "fig, ax = plt.subplots(figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a408828",
   "metadata": {},
   "source": [
    "Okay let's see what is going on and why the start argument could not be matched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6fa46afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1998-12-31', '1999-12-31', '2000-12-31', '2001-12-31',\n",
       "               '2002-12-31', '2003-12-31', '2004-12-31', '2005-12-31',\n",
       "               '2006-12-31', '2007-12-31', '2008-12-31', '2009-12-31',\n",
       "               '2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
       "               '2014-12-31', '2015-12-31', '2016-12-31'],\n",
       "              dtype='datetime64[ns]', name='year', freq='A-DEC')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d2393f2",
   "metadata": {},
   "source": [
    "***Since we resampled the data at a yearly frequency. The training data index got set at the end of the year. So we just need to change the prediction start date and end date.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b14a98dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(start=\"2017-12-31\", end=\"2025-12-31\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30136ce2",
   "metadata": {},
   "source": [
    "***Now let's plot:***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab5f4d78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "train.plot(label=\"train\")\n",
    "test.plot(label=\"test\")\n",
    "y_pred.plot(label=\"preds\")\n",
    "plt.title(\"Cumulative US Wind Capacity (GW)\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73bab7c0",
   "metadata": {},
   "source": [
    "With that there you go we have the prediction that capture the general trend in Cumulative US Wind Capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161707b0",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065fb32b",
   "metadata": {},
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   "cell_type": "markdown",
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   "id": "53ee2df0",
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   "source": [
    "\n",
    "# growth_over_year = new_data.resample(\"Y\")[\"Cumulative US Wind Capacity (GW)\"].mean()\n",
    "\n",
    "# decomposition = seasonal_decompose(growth_over_year)\n",
    "# fig = plt.figure()\n",
    "# fig = decomposition.plot()\n",
    "\n",
    "# # train = growth_over_year[\"1998-01-01\": \"2017-01-01\"].astype(float)\n",
    "# # test = growth_over_year[\"2017-01-01\" : \"2021-01-01\"].astype(float)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize = (6,4))\n",
    "# train.plot()\n",
    "# test.plot()\n",
    "\n",
    "# model = ARIMA(train, order=(1,1,1)).fit()  # Corrected parentheses\n",
    "# y_pred = model.predict(start=\"1998-12-31\", end=\"2023-12-31\")\n",
    "# fig, ax = plt.subplots(figsize=(8, 4))  # Removed extra spaces\n",
    "\n",
    "# train.plot(label=\"train\")\n",
    "# test.plot(label=\"test\")\n",
    "# y_pred.plot(label=\"preds\")\n",
    "# plt.title(\"Cumulative US Wind Capacity (GW)\")\n",
    "# ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a8afd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eb9c4bb",
   "metadata": {},
   "outputs": [],
   "source": []
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