stock_forecat_arima.ipynb

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              "            DATE TUNINDEX    INDSF    INDBQ     INDAS    INSFI    INDSC  \\\n",
              "0     02/01/2013  4621.99   3066.3  2749.78   8952.83  4704.01  3928.61   \n",
              "1     03/01/2013  4642.08  3070.24   2753.0   8990.21  4703.39  3947.45   \n",
              "2     04/01/2013  4654.33  3068.77  2753.48   8994.91  4668.97  3964.21   \n",
              "3     07/01/2013  4652.74  3070.87  2753.14   9015.93  4700.36  3968.93   \n",
              "4     08/01/2013  4634.63  3063.27  2746.39   8987.18  4690.59  3970.27   \n",
              "...          ...      ...      ...      ...       ...      ...      ...   \n",
              "1491  25/12/2018  7171.03  4900.45  4591.33  10365.92  5659.94  3079.94   \n",
              "1492  26/12/2018  7171.42   4911.0  4601.09  10457.11  5652.64  3072.55   \n",
              "1493  27/12/2018  7202.72  4932.18  4625.67   10314.5  5657.36  3064.85   \n",
              "1494  28/12/2018  7259.67  4965.97  4655.02  10462.86  5710.45  3073.92   \n",
              "1495  31/12/2018  7271.65  4962.52  4643.84  10692.08  5764.16  3062.36   \n",
              "\n",
              "        INDDI    INBCO    INAUE    INAAB    INDIN    INBMC    INDMB  \\\n",
              "0     5697.27  2663.43  2361.04   2720.7  1862.78  1308.69  2599.97   \n",
              "1     5703.18  2688.91  2454.99   2737.8   1892.5   1331.0  2596.29   \n",
              "2     5723.46  2724.14  2520.72  2767.65  1903.14  1334.89  2606.68   \n",
              "3     5724.95  2753.83  2502.98  2804.92  1870.27  1310.05  2603.49   \n",
              "4     5729.97   2721.8  2509.74  2764.15  1861.52  1302.84   2562.4   \n",
              "...       ...      ...      ...      ...      ...      ...      ...   \n",
              "1491  4782.21  7373.02  1742.67  9383.31  1391.12   682.87  3546.17   \n",
              "1492   4773.5  7343.66  1742.04   9349.3  1392.99   683.18  3510.96   \n",
              "1493  4760.85  7377.93  1746.13  9406.43  1398.69    683.2   3565.5   \n",
              "1494  4775.75   7455.1  1798.76  9477.02  1409.63   683.77  3609.65   \n",
              "1495  4760.21  7484.54  1826.76  9507.17  1417.15   685.53  3677.62   \n",
              "\n",
              "     TUNINDEX20    INPMP  \n",
              "0       1807.53      NaN  \n",
              "1        1817.9      NaN  \n",
              "2       1824.82      NaN  \n",
              "3       1827.85      NaN  \n",
              "4       1817.52      NaN  \n",
              "...         ...      ...  \n",
              "1491    3207.26  1991.77  \n",
              "1492     3206.1  1976.68  \n",
              "1493    3222.19  1962.89  \n",
              "1494    3249.43  2025.78  \n",
              "1495    3248.56  2042.56  \n",
              "\n",
              "[1496 rows x 16 columns]"
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              "      <th>DATE</th>\n",
              "      <th>TUNINDEX</th>\n",
              "      <th>INDSF</th>\n",
              "      <th>INDBQ</th>\n",
              "      <th>INDAS</th>\n",
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              "      <th>0</th>\n",
              "      <td>02/01/2013</td>\n",
              "      <td>4621.99</td>\n",
              "      <td>3066.3</td>\n",
              "      <td>2749.78</td>\n",
              "      <td>8952.83</td>\n",
              "      <td>4704.01</td>\n",
              "      <td>3928.61</td>\n",
              "      <td>5697.27</td>\n",
              "      <td>2663.43</td>\n",
              "      <td>2361.04</td>\n",
              "      <td>2720.7</td>\n",
              "      <td>1862.78</td>\n",
              "      <td>1308.69</td>\n",
              "      <td>2599.97</td>\n",
              "      <td>1807.53</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03/01/2013</td>\n",
              "      <td>4642.08</td>\n",
              "      <td>3070.24</td>\n",
              "      <td>2753.0</td>\n",
              "      <td>8990.21</td>\n",
              "      <td>4703.39</td>\n",
              "      <td>3947.45</td>\n",
              "      <td>5703.18</td>\n",
              "      <td>2688.91</td>\n",
              "      <td>2454.99</td>\n",
              "      <td>2737.8</td>\n",
              "      <td>1892.5</td>\n",
              "      <td>1331.0</td>\n",
              "      <td>2596.29</td>\n",
              "      <td>1817.9</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>04/01/2013</td>\n",
              "      <td>4654.33</td>\n",
              "      <td>3068.77</td>\n",
              "      <td>2753.48</td>\n",
              "      <td>8994.91</td>\n",
              "      <td>4668.97</td>\n",
              "      <td>3964.21</td>\n",
              "      <td>5723.46</td>\n",
              "      <td>2724.14</td>\n",
              "      <td>2520.72</td>\n",
              "      <td>2767.65</td>\n",
              "      <td>1903.14</td>\n",
              "      <td>1334.89</td>\n",
              "      <td>2606.68</td>\n",
              "      <td>1824.82</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>07/01/2013</td>\n",
              "      <td>4652.74</td>\n",
              "      <td>3070.87</td>\n",
              "      <td>2753.14</td>\n",
              "      <td>9015.93</td>\n",
              "      <td>4700.36</td>\n",
              "      <td>3968.93</td>\n",
              "      <td>5724.95</td>\n",
              "      <td>2753.83</td>\n",
              "      <td>2502.98</td>\n",
              "      <td>2804.92</td>\n",
              "      <td>1870.27</td>\n",
              "      <td>1310.05</td>\n",
              "      <td>2603.49</td>\n",
              "      <td>1827.85</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>08/01/2013</td>\n",
              "      <td>4634.63</td>\n",
              "      <td>3063.27</td>\n",
              "      <td>2746.39</td>\n",
              "      <td>8987.18</td>\n",
              "      <td>4690.59</td>\n",
              "      <td>3970.27</td>\n",
              "      <td>5729.97</td>\n",
              "      <td>2721.8</td>\n",
              "      <td>2509.74</td>\n",
              "      <td>2764.15</td>\n",
              "      <td>1861.52</td>\n",
              "      <td>1302.84</td>\n",
              "      <td>2562.4</td>\n",
              "      <td>1817.52</td>\n",
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              "    <tr>\n",
              "      <th>1491</th>\n",
              "      <td>25/12/2018</td>\n",
              "      <td>7171.03</td>\n",
              "      <td>4900.45</td>\n",
              "      <td>4591.33</td>\n",
              "      <td>10365.92</td>\n",
              "      <td>5659.94</td>\n",
              "      <td>3079.94</td>\n",
              "      <td>4782.21</td>\n",
              "      <td>7373.02</td>\n",
              "      <td>1742.67</td>\n",
              "      <td>9383.31</td>\n",
              "      <td>1391.12</td>\n",
              "      <td>682.87</td>\n",
              "      <td>3546.17</td>\n",
              "      <td>3207.26</td>\n",
              "      <td>1991.77</td>\n",
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              "    <tr>\n",
              "      <th>1492</th>\n",
              "      <td>26/12/2018</td>\n",
              "      <td>7171.42</td>\n",
              "      <td>4911.0</td>\n",
              "      <td>4601.09</td>\n",
              "      <td>10457.11</td>\n",
              "      <td>5652.64</td>\n",
              "      <td>3072.55</td>\n",
              "      <td>4773.5</td>\n",
              "      <td>7343.66</td>\n",
              "      <td>1742.04</td>\n",
              "      <td>9349.3</td>\n",
              "      <td>1392.99</td>\n",
              "      <td>683.18</td>\n",
              "      <td>3510.96</td>\n",
              "      <td>3206.1</td>\n",
              "      <td>1976.68</td>\n",
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              "      <th>1493</th>\n",
              "      <td>27/12/2018</td>\n",
              "      <td>7202.72</td>\n",
              "      <td>4932.18</td>\n",
              "      <td>4625.67</td>\n",
              "      <td>10314.5</td>\n",
              "      <td>5657.36</td>\n",
              "      <td>3064.85</td>\n",
              "      <td>4760.85</td>\n",
              "      <td>7377.93</td>\n",
              "      <td>1746.13</td>\n",
              "      <td>9406.43</td>\n",
              "      <td>1398.69</td>\n",
              "      <td>683.2</td>\n",
              "      <td>3565.5</td>\n",
              "      <td>3222.19</td>\n",
              "      <td>1962.89</td>\n",
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              "      <th>1494</th>\n",
              "      <td>28/12/2018</td>\n",
              "      <td>7259.67</td>\n",
              "      <td>4965.97</td>\n",
              "      <td>4655.02</td>\n",
              "      <td>10462.86</td>\n",
              "      <td>5710.45</td>\n",
              "      <td>3073.92</td>\n",
              "      <td>4775.75</td>\n",
              "      <td>7455.1</td>\n",
              "      <td>1798.76</td>\n",
              "      <td>9477.02</td>\n",
              "      <td>1409.63</td>\n",
              "      <td>683.77</td>\n",
              "      <td>3609.65</td>\n",
              "      <td>3249.43</td>\n",
              "      <td>2025.78</td>\n",
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              "      <th>1495</th>\n",
              "      <td>31/12/2018</td>\n",
              "      <td>7271.65</td>\n",
              "      <td>4962.52</td>\n",
              "      <td>4643.84</td>\n",
              "      <td>10692.08</td>\n",
              "      <td>5764.16</td>\n",
              "      <td>3062.36</td>\n",
              "      <td>4760.21</td>\n",
              "      <td>7484.54</td>\n",
              "      <td>1826.76</td>\n",
              "      <td>9507.17</td>\n",
              "      <td>1417.15</td>\n",
              "      <td>685.53</td>\n",
              "      <td>3677.62</td>\n",
              "      <td>3248.56</td>\n",
              "      <td>2042.56</td>\n",
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              "<p>1496 rows × 16 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/drive/MyDrive/dataset ml/stock_tunisia/index_stock_tunisia.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        },
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        "outputId": "c33558bc-cd45-4336-ac7b-c2d9cd5e870a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1496 entries, 0 to 1495\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   DATE        1496 non-null   object\n",
            " 1   TUNINDEX    1496 non-null   object\n",
            " 2   INDSF       1496 non-null   object\n",
            " 3   INDBQ       1496 non-null   object\n",
            " 4   INDAS       1496 non-null   object\n",
            " 5   INSFI       1496 non-null   object\n",
            " 6   INDSC       1496 non-null   object\n",
            " 7   INDDI       1496 non-null   object\n",
            " 8   INBCO       1496 non-null   object\n",
            " 9   INAUE       1496 non-null   object\n",
            " 10  INAAB       1496 non-null   object\n",
            " 11  INDIN       1496 non-null   object\n",
            " 12  INBMC       1496 non-null   object\n",
            " 13  INDMB       1496 non-null   object\n",
            " 14  TUNINDEX20  1496 non-null   object\n",
            " 15  INPMP       1001 non-null   object\n",
            "dtypes: object(16)\n",
            "memory usage: 187.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "data cleaning"
      ],
      "metadata": {
        "id": "dRxxE6x7aRme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('INPMP',axis=1)"
      ],
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          "height": 424
        },
        "id": "I5UcpdYt0Vaj",
        "outputId": "677996a4-258c-4069-ea54-680f13359958"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            DATE TUNINDEX    INDSF    INDBQ     INDAS    INSFI    INDSC  \\\n",
              "0     02/01/2013  4621.99   3066.3  2749.78   8952.83  4704.01  3928.61   \n",
              "1     03/01/2013  4642.08  3070.24   2753.0   8990.21  4703.39  3947.45   \n",
              "2     04/01/2013  4654.33  3068.77  2753.48   8994.91  4668.97  3964.21   \n",
              "3     07/01/2013  4652.74  3070.87  2753.14   9015.93  4700.36  3968.93   \n",
              "4     08/01/2013  4634.63  3063.27  2746.39   8987.18  4690.59  3970.27   \n",
              "...          ...      ...      ...      ...       ...      ...      ...   \n",
              "1491  25/12/2018  7171.03  4900.45  4591.33  10365.92  5659.94  3079.94   \n",
              "1492  26/12/2018  7171.42   4911.0  4601.09  10457.11  5652.64  3072.55   \n",
              "1493  27/12/2018  7202.72  4932.18  4625.67   10314.5  5657.36  3064.85   \n",
              "1494  28/12/2018  7259.67  4965.97  4655.02  10462.86  5710.45  3073.92   \n",
              "1495  31/12/2018  7271.65  4962.52  4643.84  10692.08  5764.16  3062.36   \n",
              "\n",
              "        INDDI    INBCO    INAUE    INAAB    INDIN    INBMC    INDMB TUNINDEX20  \n",
              "0     5697.27  2663.43  2361.04   2720.7  1862.78  1308.69  2599.97    1807.53  \n",
              "1     5703.18  2688.91  2454.99   2737.8   1892.5   1331.0  2596.29     1817.9  \n",
              "2     5723.46  2724.14  2520.72  2767.65  1903.14  1334.89  2606.68    1824.82  \n",
              "3     5724.95  2753.83  2502.98  2804.92  1870.27  1310.05  2603.49    1827.85  \n",
              "4     5729.97   2721.8  2509.74  2764.15  1861.52  1302.84   2562.4    1817.52  \n",
              "...       ...      ...      ...      ...      ...      ...      ...        ...  \n",
              "1491  4782.21  7373.02  1742.67  9383.31  1391.12   682.87  3546.17    3207.26  \n",
              "1492   4773.5  7343.66  1742.04   9349.3  1392.99   683.18  3510.96     3206.1  \n",
              "1493  4760.85  7377.93  1746.13  9406.43  1398.69    683.2   3565.5    3222.19  \n",
              "1494  4775.75   7455.1  1798.76  9477.02  1409.63   683.77  3609.65    3249.43  \n",
              "1495  4760.21  7484.54  1826.76  9507.17  1417.15   685.53  3677.62    3248.56  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TUNINDEX</th>\n",
              "      <th>INDSF</th>\n",
              "      <th>INDBQ</th>\n",
              "      <th>INDAS</th>\n",
              "      <th>INSFI</th>\n",
              "      <th>INDSC</th>\n",
              "      <th>INDDI</th>\n",
              "      <th>INBCO</th>\n",
              "      <th>INAUE</th>\n",
              "      <th>INAAB</th>\n",
              "      <th>INDIN</th>\n",
              "      <th>INBMC</th>\n",
              "      <th>INDMB</th>\n",
              "      <th>TUNINDEX20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02/01/2013</td>\n",
              "      <td>4621.99</td>\n",
              "      <td>3066.3</td>\n",
              "      <td>2749.78</td>\n",
              "      <td>8952.83</td>\n",
              "      <td>4704.01</td>\n",
              "      <td>3928.61</td>\n",
              "      <td>5697.27</td>\n",
              "      <td>2663.43</td>\n",
              "      <td>2361.04</td>\n",
              "      <td>2720.7</td>\n",
              "      <td>1862.78</td>\n",
              "      <td>1308.69</td>\n",
              "      <td>2599.97</td>\n",
              "      <td>1807.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03/01/2013</td>\n",
              "      <td>4642.08</td>\n",
              "      <td>3070.24</td>\n",
              "      <td>2753.0</td>\n",
              "      <td>8990.21</td>\n",
              "      <td>4703.39</td>\n",
              "      <td>3947.45</td>\n",
              "      <td>5703.18</td>\n",
              "      <td>2688.91</td>\n",
              "      <td>2454.99</td>\n",
              "      <td>2737.8</td>\n",
              "      <td>1892.5</td>\n",
              "      <td>1331.0</td>\n",
              "      <td>2596.29</td>\n",
              "      <td>1817.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>04/01/2013</td>\n",
              "      <td>4654.33</td>\n",
              "      <td>3068.77</td>\n",
              "      <td>2753.48</td>\n",
              "      <td>8994.91</td>\n",
              "      <td>4668.97</td>\n",
              "      <td>3964.21</td>\n",
              "      <td>5723.46</td>\n",
              "      <td>2724.14</td>\n",
              "      <td>2520.72</td>\n",
              "      <td>2767.65</td>\n",
              "      <td>1903.14</td>\n",
              "      <td>1334.89</td>\n",
              "      <td>2606.68</td>\n",
              "      <td>1824.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>07/01/2013</td>\n",
              "      <td>4652.74</td>\n",
              "      <td>3070.87</td>\n",
              "      <td>2753.14</td>\n",
              "      <td>9015.93</td>\n",
              "      <td>4700.36</td>\n",
              "      <td>3968.93</td>\n",
              "      <td>5724.95</td>\n",
              "      <td>2753.83</td>\n",
              "      <td>2502.98</td>\n",
              "      <td>2804.92</td>\n",
              "      <td>1870.27</td>\n",
              "      <td>1310.05</td>\n",
              "      <td>2603.49</td>\n",
              "      <td>1827.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>08/01/2013</td>\n",
              "      <td>4634.63</td>\n",
              "      <td>3063.27</td>\n",
              "      <td>2746.39</td>\n",
              "      <td>8987.18</td>\n",
              "      <td>4690.59</td>\n",
              "      <td>3970.27</td>\n",
              "      <td>5729.97</td>\n",
              "      <td>2721.8</td>\n",
              "      <td>2509.74</td>\n",
              "      <td>2764.15</td>\n",
              "      <td>1861.52</td>\n",
              "      <td>1302.84</td>\n",
              "      <td>2562.4</td>\n",
              "      <td>1817.52</td>\n",
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              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1491</th>\n",
              "      <td>25/12/2018</td>\n",
              "      <td>7171.03</td>\n",
              "      <td>4900.45</td>\n",
              "      <td>4591.33</td>\n",
              "      <td>10365.92</td>\n",
              "      <td>5659.94</td>\n",
              "      <td>3079.94</td>\n",
              "      <td>4782.21</td>\n",
              "      <td>7373.02</td>\n",
              "      <td>1742.67</td>\n",
              "      <td>9383.31</td>\n",
              "      <td>1391.12</td>\n",
              "      <td>682.87</td>\n",
              "      <td>3546.17</td>\n",
              "      <td>3207.26</td>\n",
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              "    <tr>\n",
              "      <th>1492</th>\n",
              "      <td>26/12/2018</td>\n",
              "      <td>7171.42</td>\n",
              "      <td>4911.0</td>\n",
              "      <td>4601.09</td>\n",
              "      <td>10457.11</td>\n",
              "      <td>5652.64</td>\n",
              "      <td>3072.55</td>\n",
              "      <td>4773.5</td>\n",
              "      <td>7343.66</td>\n",
              "      <td>1742.04</td>\n",
              "      <td>9349.3</td>\n",
              "      <td>1392.99</td>\n",
              "      <td>683.18</td>\n",
              "      <td>3510.96</td>\n",
              "      <td>3206.1</td>\n",
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              "    <tr>\n",
              "      <th>1493</th>\n",
              "      <td>27/12/2018</td>\n",
              "      <td>7202.72</td>\n",
              "      <td>4932.18</td>\n",
              "      <td>4625.67</td>\n",
              "      <td>10314.5</td>\n",
              "      <td>5657.36</td>\n",
              "      <td>3064.85</td>\n",
              "      <td>4760.85</td>\n",
              "      <td>7377.93</td>\n",
              "      <td>1746.13</td>\n",
              "      <td>9406.43</td>\n",
              "      <td>1398.69</td>\n",
              "      <td>683.2</td>\n",
              "      <td>3565.5</td>\n",
              "      <td>3222.19</td>\n",
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              "    <tr>\n",
              "      <th>1494</th>\n",
              "      <td>28/12/2018</td>\n",
              "      <td>7259.67</td>\n",
              "      <td>4965.97</td>\n",
              "      <td>4655.02</td>\n",
              "      <td>10462.86</td>\n",
              "      <td>5710.45</td>\n",
              "      <td>3073.92</td>\n",
              "      <td>4775.75</td>\n",
              "      <td>7455.1</td>\n",
              "      <td>1798.76</td>\n",
              "      <td>9477.02</td>\n",
              "      <td>1409.63</td>\n",
              "      <td>683.77</td>\n",
              "      <td>3609.65</td>\n",
              "      <td>3249.43</td>\n",
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              "    <tr>\n",
              "      <th>1495</th>\n",
              "      <td>31/12/2018</td>\n",
              "      <td>7271.65</td>\n",
              "      <td>4962.52</td>\n",
              "      <td>4643.84</td>\n",
              "      <td>10692.08</td>\n",
              "      <td>5764.16</td>\n",
              "      <td>3062.36</td>\n",
              "      <td>4760.21</td>\n",
              "      <td>7484.54</td>\n",
              "      <td>1826.76</td>\n",
              "      <td>9507.17</td>\n",
              "      <td>1417.15</td>\n",
              "      <td>685.53</td>\n",
              "      <td>3677.62</td>\n",
              "      <td>3248.56</td>\n",
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              "<p>1496 rows × 15 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns[1:]:\n",
        "  df[i]=df[i].apply(lambda X : float(str(X).replace(',','.')) )\n",
        " \n"
      ],
      "metadata": {
        "id": "ZT4WP7oqzhWO"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ploting data"
      ],
      "metadata": {
        "id": "yQgjXOjIaUdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df.TUNINDEX.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ELvqbvGr3u42",
        "outputId": "00e3bff8-8dbf-468c-8792-c572d1028b2a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51d02d8650>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(20,15))\n",
        "df.drop('DATE',axis=1).plot()\n",
        "plt.show"
      ],
      "metadata": {
        "id": "UMDEboDP37US",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "6e9cd797-a09f-4ad9-abc2-82523ec7da9f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "create TUNINDEX time series"
      ],
      "metadata": {
        "id": "0Uvv-0H9aZ9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=df[['DATE','TUNINDEX']]\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0tn7t2cL2_dK",
        "outputId": "68b30715-fb2b-478d-b175-ea0ebb0d2053"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         DATE  TUNINDEX\n",
              "0  02/01/2013   4621.99\n",
              "1  03/01/2013   4642.08\n",
              "2  04/01/2013   4654.33\n",
              "3  07/01/2013   4652.74\n",
              "4  08/01/2013   4634.63"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81504a39-e18a-4607-92ca-b363dfbf795e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TUNINDEX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02/01/2013</td>\n",
              "      <td>4621.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03/01/2013</td>\n",
              "      <td>4642.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>04/01/2013</td>\n",
              "      <td>4654.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>07/01/2013</td>\n",
              "      <td>4652.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>08/01/2013</td>\n",
              "      <td>4634.63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81504a39-e18a-4607-92ca-b363dfbf795e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81504a39-e18a-4607-92ca-b363dfbf795e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81504a39-e18a-4607-92ca-b363dfbf795e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['DATE']=pd.to_datetime(data['DATE'])\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "rWYAXli95_qQ",
        "outputId": "0a87fca1-02bc-4053-be09-f565e441f569"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        DATE  TUNINDEX\n",
              "0 2013-02-01   4621.99\n",
              "1 2013-03-01   4642.08\n",
              "2 2013-04-01   4654.33\n",
              "3 2013-07-01   4652.74\n",
              "4 2013-08-01   4634.63"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-663ae475-2ecf-4d89-8136-78ff9700a85e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TUNINDEX</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-02-01</td>\n",
              "      <td>4621.99</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-03-01</td>\n",
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              "      <th>2</th>\n",
              "      <td>2013-04-01</td>\n",
              "      <td>4654.33</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-07-01</td>\n",
              "      <td>4652.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-08-01</td>\n",
              "      <td>4634.63</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-663ae475-2ecf-4d89-8136-78ff9700a85e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
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        "data.info()"
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        "outputId": "98ea7f48-8c17-4b96-acdb-6d6f24b76039"
      },
      "execution_count": 47,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1496 entries, 0 to 1495\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   DATE      1496 non-null   datetime64[ns]\n",
            " 1   TUNINDEX  1496 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 23.5 KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "data.set_index('DATE',inplace=True)"
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      "metadata": {
        "id": "TwS2uAi06j9i"
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      "execution_count": 50,
      "outputs": []
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      "execution_count": 51,
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              "            TUNINDEX\n",
              "DATE                \n",
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              "2013-07-01   4652.74\n",
              "2013-08-01   4634.63\n",
              "...              ...\n",
              "2018-12-25   7171.03\n",
              "2018-12-26   7171.42\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      "execution_count": 12,
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              "          TUNINDEX\n",
              "count  1496.000000\n",
              "mean   5551.538750\n",
              "std     986.776866\n",
              "min    4337.670000\n",
              "25%    4665.945000\n",
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              "75%    6077.510000\n",
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              "      <td>5551.538750</td>\n",
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              "      <td>986.776866</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "n4Wt3DnF64YV",
        "outputId": "e3ed32cd-ddd4-43c6-bad1-a059b3b7b4ad"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51ad2c7c90>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check stationarity"
      ],
      "metadata": {
        "id": "BFFAtL25ahYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller"
      ],
      "metadata": {
        "id": "lWXDL3GS6_88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b62feed6-f253-42c1-97a2-a5be8bc79988"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adfuller(data['TUNINDEX'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldQhXocy9ls8",
        "outputId": "733acd13-8488-48ef-bdaf-c941b698ef95"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.0808852506380418,\n",
              " 0.9513094668238564,\n",
              " 10,\n",
              " 1485,\n",
              " {'1%': -3.43476120520139,\n",
              "  '10%': -2.567807233388883,\n",
              "  '5%': -2.863488262178673},\n",
              " 13706.38291936124)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p-value is bigger then 0.05-->not stationary-->creating returns"
      ],
      "metadata": {
        "id": "q_O9BT-Jal8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diff=data.diff()\n",
        "diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "khif4U_FGz6Z",
        "outputId": "0fdecfa2-2530-4c36-c2b3-4eee2c6be28b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            TUNINDEX\n",
              "DATE                \n",
              "2013-02-01       NaN\n",
              "2013-03-01     20.09\n",
              "2013-04-01     12.25\n",
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              "2013-08-01    -18.11\n",
              "...              ...\n",
              "2018-12-25    -31.93\n",
              "2018-12-26      0.39\n",
              "2018-12-27     31.30\n",
              "2018-12-28     56.95\n",
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              "      <td>-31.93</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diff.iloc[1:].plot(figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "K7lSZKjFInKn",
        "outputId": "c9e566bb-c1f9-4a22-ef9f-80179cb96aa7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51bee91890>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adfuller(diff['TUNINDEX'].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15-IA2HNHnoy",
        "outputId": "15ac0268-2e64-436e-9dc4-c17edf0305af"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-9.722652337157456,\n",
              " 9.453396003951872e-17,\n",
              " 9,\n",
              " 1485,\n",
              " {'1%': -3.43476120520139,\n",
              "  '10%': -2.567807233388883,\n",
              "  '5%': -2.863488262178673},\n",
              " 13694.581782949947)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "p-value <0.05 -->stationary"
      ],
      "metadata": {
        "id": "VCcwk-Dnazsl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ploting pacf and acf for retuns to determine the model parameters"
      ],
      "metadata": {
        "id": "6qaE_r5VbGu6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.graphics.tsaplots as sgt"
      ],
      "metadata": {
        "id": "pE04PmTKJG7p"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sgt.plot_pacf(diff.TUNINDEX[1:],lags=40,zero=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "d75e-KBaJMSa",
        "outputId": "e7898555-0400-4950-c49a-84df1961eb94"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sgt.plot_acf(diff.TUNINDEX[1:],lags=40,zero=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "psQJOv5JK8ZI",
        "outputId": "b73d5f60-8b1d-42fd-f27e-5270b198417f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "split data"
      ],
      "metadata": {
        "id": "2x8zEKzNbS8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data=data[:int(0.8*len(data))]\n",
        "test_data=data[int(0.8*len(data)):]"
      ],
      "metadata": {
        "id": "MwRPoaYnXp53"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating model based on acf and pacf"
      ],
      "metadata": {
        "id": "9VaiP5tebUiE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA\n"
      ],
      "metadata": {
        "id": "IyIJAL_8H6Rg"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "ktGvoCaCVp6A"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=ARIMA(data,order=(8,1,6))\n",
        "model_fit=model.fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOXj835pTP5G",
        "outputId": "9bba131f-d762-46f2-a61d-6cb55bbf7447"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "QUVM4n6MTYsA",
        "outputId": "7dd3dfc8-5b1b-4a99-916a-422e003001f6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:             D.TUNINDEX   No. Observations:                 1495\n",
              "Model:                 ARIMA(8, 1, 6)   Log Likelihood               -6964.174\n",
              "Method:                       css-mle   S.D. of innovations             25.514\n",
              "Date:                Sat, 26 Mar 2022   AIC                          13960.347\n",
              "Time:                        17:39:23   BIC                          14045.305\n",
              "Sample:                             1   HQIC                         13992.002\n",
              "                                                                              \n",
              "====================================================================================\n",
              "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------\n",
              "const                1.7878      1.436      1.245      0.213      -1.028       4.603\n",
              "ar.L1.D.TUNINDEX     0.3280      0.279      1.177      0.239      -0.218       0.874\n",
              "ar.L2.D.TUNINDEX    -0.1830      0.199     -0.919      0.358      -0.574       0.207\n",
              "ar.L3.D.TUNINDEX    -0.2816      0.180     -1.564      0.118      -0.635       0.071\n",
              "ar.L4.D.TUNINDEX     0.4684      0.182      2.577      0.010       0.112       0.825\n",
              "ar.L5.D.TUNINDEX     0.2315      0.197      1.173      0.241      -0.155       0.618\n",
              "ar.L6.D.TUNINDEX     0.2984      0.234      1.274      0.203      -0.161       0.757\n",
              "ar.L7.D.TUNINDEX    -0.1144      0.036     -3.156      0.002      -0.185      -0.043\n",
              "ar.L8.D.TUNINDEX     0.0869      0.032      2.707      0.007       0.024       0.150\n",
              "ma.L1.D.TUNINDEX    -0.1723      0.279     -0.617      0.537      -0.720       0.375\n",
              "ma.L2.D.TUNINDEX     0.1749      0.190      0.922      0.357      -0.197       0.547\n",
              "ma.L3.D.TUNINDEX     0.3529      0.159      2.224      0.026       0.042       0.664\n",
              "ma.L4.D.TUNINDEX    -0.4542      0.183     -2.478      0.013      -0.813      -0.095\n",
              "ma.L5.D.TUNINDEX    -0.2483      0.184     -1.348      0.178      -0.609       0.113\n",
              "ma.L6.D.TUNINDEX    -0.2887      0.231     -1.248      0.212      -0.742       0.165\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1            1.0396           -0.0000j            1.0396           -0.0000\n",
              "AR.2            0.3356           -0.9557j            1.0129           -0.1962\n",
              "AR.3            0.3356           +0.9557j            1.0129            0.1962\n",
              "AR.4           -1.0935           -0.0000j            1.0935           -0.5000\n",
              "AR.5           -0.8213           -1.1333j            1.3996           -0.3498\n",
              "AR.6           -0.8213           +1.1333j            1.3996            0.3498\n",
              "AR.7            1.1707           -1.9147j            2.2442           -0.1627\n",
              "AR.8            1.1707           +1.9147j            2.2442            0.1627\n",
              "MA.1            1.0842           -0.0000j            1.0842           -0.0000\n",
              "MA.2            0.3415           -0.9496j            1.0091           -0.1951\n",
              "MA.3            0.3415           +0.9496j            1.0091            0.1951\n",
              "MA.4           -1.1354           -0.0000j            1.1354           -0.5000\n",
              "MA.5           -0.7460           -1.4856j            1.6624           -0.3241\n",
              "MA.6           -0.7460           +1.4856j            1.6624            0.3241\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>    <td>D.TUNINDEX</td>    <th>  No. Observations:  </th>   <td>1495</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(8, 1, 6)</td>  <th>  Log Likelihood     </th> <td>-6964.174</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>25.514</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Sat, 26 Mar 2022</td> <th>  AIC                </th> <td>13960.347</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>17:39:23</td>     <th>  BIC                </th> <td>14045.305</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>13992.002</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>            <td>    1.7878</td> <td>    1.436</td> <td>    1.245</td> <td> 0.213</td> <td>   -1.028</td> <td>    4.603</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D.TUNINDEX</th> <td>    0.3280</td> <td>    0.279</td> <td>    1.177</td> <td> 0.239</td> <td>   -0.218</td> <td>    0.874</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2.D.TUNINDEX</th> <td>   -0.1830</td> <td>    0.199</td> <td>   -0.919</td> <td> 0.358</td> <td>   -0.574</td> <td>    0.207</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L3.D.TUNINDEX</th> <td>   -0.2816</td> <td>    0.180</td> <td>   -1.564</td> <td> 0.118</td> <td>   -0.635</td> <td>    0.071</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L4.D.TUNINDEX</th> <td>    0.4684</td> <td>    0.182</td> <td>    2.577</td> <td> 0.010</td> <td>    0.112</td> <td>    0.825</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L5.D.TUNINDEX</th> <td>    0.2315</td> <td>    0.197</td> <td>    1.173</td> <td> 0.241</td> <td>   -0.155</td> <td>    0.618</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L6.D.TUNINDEX</th> <td>    0.2984</td> <td>    0.234</td> <td>    1.274</td> <td> 0.203</td> <td>   -0.161</td> <td>    0.757</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L7.D.TUNINDEX</th> <td>   -0.1144</td> <td>    0.036</td> <td>   -3.156</td> <td> 0.002</td> <td>   -0.185</td> <td>   -0.043</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L8.D.TUNINDEX</th> <td>    0.0869</td> <td>    0.032</td> <td>    2.707</td> <td> 0.007</td> <td>    0.024</td> <td>    0.150</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.TUNINDEX</th> <td>   -0.1723</td> <td>    0.279</td> <td>   -0.617</td> <td> 0.537</td> <td>   -0.720</td> <td>    0.375</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2.D.TUNINDEX</th> <td>    0.1749</td> <td>    0.190</td> <td>    0.922</td> <td> 0.357</td> <td>   -0.197</td> <td>    0.547</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3.D.TUNINDEX</th> <td>    0.3529</td> <td>    0.159</td> <td>    2.224</td> <td> 0.026</td> <td>    0.042</td> <td>    0.664</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L4.D.TUNINDEX</th> <td>   -0.4542</td> <td>    0.183</td> <td>   -2.478</td> <td> 0.013</td> <td>   -0.813</td> <td>   -0.095</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L5.D.TUNINDEX</th> <td>   -0.2483</td> <td>    0.184</td> <td>   -1.348</td> <td> 0.178</td> <td>   -0.609</td> <td>    0.113</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L6.D.TUNINDEX</th> <td>   -0.2887</td> <td>    0.231</td> <td>   -1.248</td> <td> 0.212</td> <td>   -0.742</td> <td>    0.165</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>           1.0396</td> <td>          -0.0000j</td> <td>           1.0396</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.2</th> <td>           0.3356</td> <td>          -0.9557j</td> <td>           1.0129</td> <td>          -0.1962</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.3</th> <td>           0.3356</td> <td>          +0.9557j</td> <td>           1.0129</td> <td>           0.1962</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.4</th> <td>          -1.0935</td> <td>          -0.0000j</td> <td>           1.0935</td> <td>          -0.5000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.5</th> <td>          -0.8213</td> <td>          -1.1333j</td> <td>           1.3996</td> <td>          -0.3498</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.6</th> <td>          -0.8213</td> <td>          +1.1333j</td> <td>           1.3996</td> <td>           0.3498</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.7</th> <td>           1.1707</td> <td>          -1.9147j</td> <td>           2.2442</td> <td>          -0.1627</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.8</th> <td>           1.1707</td> <td>          +1.9147j</td> <td>           2.2442</td> <td>           0.1627</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.0842</td> <td>          -0.0000j</td> <td>           1.0842</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.2</th> <td>           0.3415</td> <td>          -0.9496j</td> <td>           1.0091</td> <td>          -0.1951</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.3</th> <td>           0.3415</td> <td>          +0.9496j</td> <td>           1.0091</td> <td>           0.1951</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.4</th> <td>          -1.1354</td> <td>          -0.0000j</td> <td>           1.1354</td> <td>          -0.5000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.5</th> <td>          -0.7460</td> <td>          -1.4856j</td> <td>           1.6624</td> <td>          -0.3241</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.6</th> <td>          -0.7460</td> <td>          +1.4856j</td> <td>           1.6624</td> <td>           0.3241</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "most of the p values are big , so we will try a less complicated model"
      ],
      "metadata": {
        "id": "qmazSZYHbbUl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=ARIMA(train_data,order=(8,1,4))\n",
        "model_fit=model.fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MowZzAQMT61F",
        "outputId": "d3e1680e-3f76-4a19-b0ee-2d89a12b33d2"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "xoGmtNWnUOne",
        "outputId": "32cae4ba-9f40-4169-a610-bb0a116bfba7"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:             D.TUNINDEX   No. Observations:                 1195\n",
              "Model:                 ARIMA(8, 1, 4)   Log Likelihood               -5302.923\n",
              "Method:                       css-mle   S.D. of innovations             20.432\n",
              "Date:                Sat, 26 Mar 2022   AIC                          10633.847\n",
              "Time:                        18:00:10   BIC                          10705.049\n",
              "Sample:                             1   HQIC                         10660.674\n",
              "                                                                              \n",
              "====================================================================================\n",
              "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------\n",
              "const                1.1995      0.867      1.383      0.167      -0.500       2.899\n",
              "ar.L1.D.TUNINDEX     1.2328      0.054     22.684      0.000       1.126       1.339\n",
              "ar.L2.D.TUNINDEX    -0.2958      0.112     -2.652      0.008      -0.514      -0.077\n",
              "ar.L3.D.TUNINDEX     0.8954      0.109      8.184      0.000       0.681       1.110\n",
              "ar.L4.D.TUNINDEX    -1.1723      0.074    -15.747      0.000      -1.318      -1.026\n",
              "ar.L5.D.TUNINDEX     0.2280      0.055      4.175      0.000       0.121       0.335\n",
              "ar.L6.D.TUNINDEX    -0.0147      0.054     -0.271      0.787      -0.121       0.092\n",
              "ar.L7.D.TUNINDEX     0.0373      0.054      0.686      0.493      -0.069       0.144\n",
              "ar.L8.D.TUNINDEX    -0.0058      0.032     -0.184      0.854      -0.068       0.056\n",
              "ma.L1.D.TUNINDEX    -1.0311      0.050    -20.535      0.000      -1.129      -0.933\n",
              "ma.L2.D.TUNINDEX     0.1361      0.093      1.465      0.143      -0.046       0.318\n",
              "ma.L3.D.TUNINDEX    -0.8948      0.085    -10.579      0.000      -1.061      -0.729\n",
              "ma.L4.D.TUNINDEX     0.9293      0.044     20.991      0.000       0.843       1.016\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1           -0.5139           -0.9003j            1.0366           -0.3325\n",
              "AR.2           -0.5139           +0.9003j            1.0366            0.3325\n",
              "AR.3            0.9865           -0.2193j            1.0106           -0.0348\n",
              "AR.4            0.9865           +0.2193j            1.0106            0.0348\n",
              "AR.5           -1.6205           -2.7671j            3.2067           -0.3343\n",
              "AR.6           -1.6205           +2.7671j            3.2067            0.3343\n",
              "AR.7            2.4234           -0.0000j            2.4234           -0.0000\n",
              "AR.8            6.2566           -0.0000j            6.2566           -0.0000\n",
              "MA.1           -0.4943           -0.9116j            1.0370           -0.3291\n",
              "MA.2           -0.4943           +0.9116j            1.0370            0.3291\n",
              "MA.3            0.9758           -0.2201j            1.0003           -0.0353\n",
              "MA.4            0.9758           +0.2201j            1.0003            0.0353\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>    <td>D.TUNINDEX</td>    <th>  No. Observations:  </th>   <td>1195</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(8, 1, 4)</td>  <th>  Log Likelihood     </th> <td>-5302.923</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>20.432</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Sat, 26 Mar 2022</td> <th>  AIC                </th> <td>10633.847</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>18:00:10</td>     <th>  BIC                </th> <td>10705.049</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>10660.674</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>            <td>    1.1995</td> <td>    0.867</td> <td>    1.383</td> <td> 0.167</td> <td>   -0.500</td> <td>    2.899</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D.TUNINDEX</th> <td>    1.2328</td> <td>    0.054</td> <td>   22.684</td> <td> 0.000</td> <td>    1.126</td> <td>    1.339</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2.D.TUNINDEX</th> <td>   -0.2958</td> <td>    0.112</td> <td>   -2.652</td> <td> 0.008</td> <td>   -0.514</td> <td>   -0.077</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L3.D.TUNINDEX</th> <td>    0.8954</td> <td>    0.109</td> <td>    8.184</td> <td> 0.000</td> <td>    0.681</td> <td>    1.110</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L4.D.TUNINDEX</th> <td>   -1.1723</td> <td>    0.074</td> <td>  -15.747</td> <td> 0.000</td> <td>   -1.318</td> <td>   -1.026</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L5.D.TUNINDEX</th> <td>    0.2280</td> <td>    0.055</td> <td>    4.175</td> <td> 0.000</td> <td>    0.121</td> <td>    0.335</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L6.D.TUNINDEX</th> <td>   -0.0147</td> <td>    0.054</td> <td>   -0.271</td> <td> 0.787</td> <td>   -0.121</td> <td>    0.092</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L7.D.TUNINDEX</th> <td>    0.0373</td> <td>    0.054</td> <td>    0.686</td> <td> 0.493</td> <td>   -0.069</td> <td>    0.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L8.D.TUNINDEX</th> <td>   -0.0058</td> <td>    0.032</td> <td>   -0.184</td> <td> 0.854</td> <td>   -0.068</td> <td>    0.056</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.TUNINDEX</th> <td>   -1.0311</td> <td>    0.050</td> <td>  -20.535</td> <td> 0.000</td> <td>   -1.129</td> <td>   -0.933</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2.D.TUNINDEX</th> <td>    0.1361</td> <td>    0.093</td> <td>    1.465</td> <td> 0.143</td> <td>   -0.046</td> <td>    0.318</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3.D.TUNINDEX</th> <td>   -0.8948</td> <td>    0.085</td> <td>  -10.579</td> <td> 0.000</td> <td>   -1.061</td> <td>   -0.729</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L4.D.TUNINDEX</th> <td>    0.9293</td> <td>    0.044</td> <td>   20.991</td> <td> 0.000</td> <td>    0.843</td> <td>    1.016</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>          -0.5139</td> <td>          -0.9003j</td> <td>           1.0366</td> <td>          -0.3325</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.2</th> <td>          -0.5139</td> <td>          +0.9003j</td> <td>           1.0366</td> <td>           0.3325</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.3</th> <td>           0.9865</td> <td>          -0.2193j</td> <td>           1.0106</td> <td>          -0.0348</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.4</th> <td>           0.9865</td> <td>          +0.2193j</td> <td>           1.0106</td> <td>           0.0348</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.5</th> <td>          -1.6205</td> <td>          -2.7671j</td> <td>           3.2067</td> <td>          -0.3343</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.6</th> <td>          -1.6205</td> <td>          +2.7671j</td> <td>           3.2067</td> <td>           0.3343</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.7</th> <td>           2.4234</td> <td>          -0.0000j</td> <td>           2.4234</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.8</th> <td>           6.2566</td> <td>          -0.0000j</td> <td>           6.2566</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>          -0.4943</td> <td>          -0.9116j</td> <td>           1.0370</td> <td>          -0.3291</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.2</th> <td>          -0.4943</td> <td>          +0.9116j</td> <td>           1.0370</td> <td>           0.3291</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.3</th> <td>           0.9758</td> <td>          -0.2201j</td> <td>           1.0003</td> <td>          -0.0353</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.4</th> <td>           0.9758</td> <td>          +0.2201j</td> <td>           1.0003</td> <td>           0.0353</td>\n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "doing the forecasting on the test data"
      ],
      "metadata": {
        "id": "dBkgiFFJbl9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fc, se, conf = model_fit.forecast(300, alpha=0.05) "
      ],
      "metadata": {
        "id": "YL5Q3G2-UhK9"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fc_series = pd.Series(fc, index=test_data.index)"
      ],
      "metadata": {
        "id": "Ol_9ICQzVrHS"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data['forecast']=fc_series\n",
        "test_data.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "flimQavBWYpt",
        "outputId": "744200fd-3f6f-439a-be1c-9594a0e934d9"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}