ARIMA_Model.ipynb

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    "colab": {
      "name": "ARIMA_Model.ipynb",
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      "collapsed_sections": [],
      "mount_file_id": "1n3W-8OZXXdYm3NEYlLEM2u3Y51t8_Jav",
      "authorship_tag": "ABX9TyPpyFsvJxIabvD1WbknBzRi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amanjain252002/Stock-Price-Prediction/blob/main/ARIMA_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-qNpaWK9cI2"
      },
      "source": [
        "!pip install pmdarima"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rhpi0R3g7GdO"
      },
      "source": [
        "import math\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "from statsmodels.tsa.stattools import adfuller\r\n",
        "from statsmodels.tsa.arima_model import ARIMA\r\n",
        "\r\n",
        "from pmdarima.arima import auto_arima\r\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\r\n",
        "\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkISue9H9LUn"
      },
      "source": [
        "AAPL = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_AAPL.csv\")\r\n",
        "TSLA = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_TSLA.csv\")\r\n",
        "GOOG = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_GOOG.csv\")\r\n",
        "MSFT = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_MSFT.csv\")\r\n",
        "AMZN = pd.read_csv(\"/content/drive/MyDrive/Database/Pre_Processed_AMZN.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9SHWv1OGe6T"
      },
      "source": [
        "The Dickey-Fuller test is one of the most popular statistical tests. It can be used to determine the presence of unit root in the series and help us understand if the series is stationary.\r\n",
        "\r\n",
        "**Null Hypothesis**: The series has a unit root\r\n",
        "\r\n",
        "**Alternate Hypothesis**: The series has no unit root.\r\n",
        "\r\n",
        "If we fail to reject the Null Hypothesis, then the series is non-stationary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVVghS_J-alF"
      },
      "source": [
        "def Test_Stationarity(timeseries):\r\n",
        "  result = adfuller(timeseries['Adj. Close'], autolag = 'AIC')\r\n",
        "  print(\"Results of Dickey Fuller Test\")\r\n",
        "  print(f'Test Statistics: {result[0]}')\r\n",
        "  print(f'p-value: {result[1]}')\r\n",
        "  print(f'Number of lags used: {result[2]}')\r\n",
        "  print(f'Number of observations used: {result[3]}')\r\n",
        "  for key, value in result[4].items():\r\n",
        "    print(f'critical value ({key}): {value}')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qeGKFCsyrfX"
      },
      "source": [
        "#Apple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "VVTvtUnT-MVb",
        "outputId": "ecf79b8c-553e-4c19-9d1d-8d9bffc55205"
      },
      "source": [
        "AAPL.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>27.85</td>\n",
              "      <td>27.86</td>\n",
              "      <td>26.84</td>\n",
              "      <td>27.33</td>\n",
              "      <td>24.86</td>\n",
              "      <td>212818400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>27.07</td>\n",
              "      <td>27.16</td>\n",
              "      <td>26.35</td>\n",
              "      <td>26.56</td>\n",
              "      <td>24.16</td>\n",
              "      <td>257142000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>26.64</td>\n",
              "      <td>26.86</td>\n",
              "      <td>26.16</td>\n",
              "      <td>26.57</td>\n",
              "      <td>24.16</td>\n",
              "      <td>263188400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>26.80</td>\n",
              "      <td>27.05</td>\n",
              "      <td>26.67</td>\n",
              "      <td>26.94</td>\n",
              "      <td>24.50</td>\n",
              "      <td>160423600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>27.31</td>\n",
              "      <td>28.04</td>\n",
              "      <td>27.17</td>\n",
              "      <td>27.97</td>\n",
              "      <td>25.44</td>\n",
              "      <td>237458000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close       Volume\n",
              "0  2015-01-02  27.85  27.86  26.84  27.33       24.86  212818400.0\n",
              "1  2015-01-05  27.07  27.16  26.35  26.56       24.16  257142000.0\n",
              "2  2015-01-06  26.64  26.86  26.16  26.57       24.16  263188400.0\n",
              "3  2015-01-07  26.80  27.05  26.67  26.94       24.50  160423600.0\n",
              "4  2015-01-08  27.31  28.04  27.17  27.97       25.44  237458000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuisU-jvHlV_",
        "outputId": "d5349759-1718-4703-8355-7d9f83bde793"
      },
      "source": [
        "AAPL.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMpcNnJH-H7f"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "AAPL[\"Date\"] = pd.to_datetime(AAPL[\"Date\"])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH2oGK37-S7Y",
        "outputId": "5ed5c966-879b-496a-c3fe-3e1c53e569b6"
      },
      "source": [
        "Test_Stationarity(AAPL)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey Fuller Test\n",
            "Test Statistics: 1.120722206443355\n",
            "p-value: 0.9953896550774386\n",
            "Number of lags used: 24\n",
            "Number of observations used: 1524\n",
            "critical value (1%): -3.4346481290263013\n",
            "critical value (5%): -2.8634383565975736\n",
            "critical value (10%): -2.567780658251872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XA4ZLgJtIiKK"
      },
      "source": [
        "The p-value > 0.05, so we cannot reject the Null hypothesis. Hence, we would need to use the “Integrated (I)” concept, denoted by value ‘d’ in time series, to make the data stationary while building the Auto ARIMA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef-Xz6gHLiKA"
      },
      "source": [
        "Now let's take log of the 'Adj. Close' column to reduce the magnitude of the values and reduce the series rising trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "r1NMAJkTiC9s",
        "outputId": "79d665bc-d372-4cc9-a01c-9b6c86e58023"
      },
      "source": [
        "AAPL['log Adj. Close'] = np.log(AAPL['Adj. Close'])\r\n",
        "AAPL_log_moving_avg = AAPL['log Adj. Close'].rolling(12).mean()\r\n",
        "AAPL_log_std = AAPL['log Adj. Close'].rolling(12).std()\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(AAPL['Date'], AAPL_log_moving_avg, label = \"Rolling Mean\")\r\n",
        "plt.plot(AAPL['Date'], AAPL_log_std, label = \"Rolling Std\")\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')\r\n",
        "plt.title(\"Rolling Mean and Standard Deviation\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rolling Mean and Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG_0TS7PL34W"
      },
      "source": [
        "###Split the data into training and test set\r\n",
        "\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "5-gIeq0bDrFz",
        "outputId": "157d09b0-9337-435b-b689-713b0361397a"
      },
      "source": [
        "AAPL_Train_Data = AAPL[AAPL['Date'] < '2020-10-01']\r\n",
        "AAPL_Test_Data = AAPL[AAPL['Date'] >= '2020-10-01'].reset_index(drop = True)\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(AAPL_Train_Data['Date'], AAPL_Train_Data['log Adj. Close'], label = 'Train Data')\r\n",
        "plt.plot(AAPL_Test_Data['Date'], AAPL_Test_Data['log Adj. Close'], label = 'Test Data')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff61c2f5cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfBHrHl4RSD1"
      },
      "source": [
        "###Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eimPhA-fIKrz",
        "outputId": "b9b22fc8-5d26-47d4-b9ac-399538152df7"
      },
      "source": [
        "AAPL_Auto_ARIMA_Model = auto_arima(AAPL_Train_Data['log Adj. Close'], seasonal = False,\r\n",
        "                                   error_action = 'ignore', suppress_warnings = True)\r\n",
        "print(AAPL_Auto_ARIMA_Model.summary())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 1447\n",
            "Model:               SARIMAX(1, 1, 0)   Log Likelihood                3721.627\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7437.254\n",
            "Time:                        09:13:01   BIC                          -7421.424\n",
            "Sample:                             0   HQIC                         -7431.346\n",
            "                               - 1447                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.0012      0.000      2.366      0.018       0.000       0.002\n",
            "ar.L1         -0.0981      0.015     -6.535      0.000      -0.127      -0.069\n",
            "sigma2         0.0003   6.51e-06     52.314      0.000       0.000       0.000\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              2680.38\n",
            "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               2.25   Skew:                            -0.40\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.62\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eh1Pc10YKQ5h",
        "outputId": "db094e9b-495c-44e0-a929-77983802e5c3"
      },
      "source": [
        "AAPL_ARIMA_Model = ARIMA(AAPL_Train_Data['log Adj. Close'], order = (1, 1, 0))\r\n",
        "AAPL_ARIMA_Model_Fit = AAPL_ARIMA_Model.fit()\r\n",
        "print(AAPL_ARIMA_Model_Fit.summary())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:       D.log Adj. Close   No. Observations:                 1446\n",
            "Model:                 ARIMA(1, 1, 0)   Log Likelihood                3721.627\n",
            "Method:                       css-mle   S.D. of innovations              0.018\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7437.254\n",
            "Time:                        09:13:01   BIC                          -7421.424\n",
            "Sample:                             1   HQIC                         -7431.346\n",
            "                                                                              \n",
            "==========================================================================================\n",
            "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------------\n",
            "const                      0.0011      0.000      2.405      0.016       0.000       0.002\n",
            "ar.L1.D.log Adj. Close    -0.0982      0.026     -3.748      0.000      -0.150      -0.047\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1          -10.1864           +0.0000j           10.1864            0.5000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlxQTJS1MxLU"
      },
      "source": [
        "###Predicting the closing stock price of Apple"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "iAPswyN9LSX3",
        "outputId": "e10c2ac1-954b-4b42-92c2-56cfc3683c2f"
      },
      "source": [
        "AAPL_output = AAPL_ARIMA_Model_Fit.forecast(102, alpha=0.05)\r\n",
        "AAPL_predictions = np.exp(AAPL_output[0])\r\n",
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(AAPL_Train_Data['Date'], AAPL_Train_Data['Adj. Close'], label = 'Training')\r\n",
        "plt.plot(AAPL_Test_Data['Date'], AAPL_Test_Data['Adj. Close'], label = 'Testing')\r\n",
        "plt.plot(AAPL_Test_Data['Date'], AAPL_predictions, label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff61bd5fb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcMJb9URK0dj",
        "outputId": "00a60235-8142-4a44-e7c7-27ead9558900"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(AAPL_Test_Data['Adj. Close'], AAPL_predictions))\r\n",
        "mape = np.mean(np.abs(AAPL_predictions - AAPL_Test_Data['Adj. Close'])/np.abs(AAPL_Test_Data['Adj. Close']))\r\n",
        "\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 6.4063833564144845\n",
            "MAPE: 0.03885097141631591\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1W_Gggpy0kg"
      },
      "source": [
        "#Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "pJWCwitvoSas",
        "outputId": "b955a26f-3b2f-4957-fa78-31a5dbb3a5ec"
      },
      "source": [
        "TSLA.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>44.57</td>\n",
              "      <td>44.65</td>\n",
              "      <td>42.65</td>\n",
              "      <td>43.86</td>\n",
              "      <td>43.86</td>\n",
              "      <td>23822000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>42.91</td>\n",
              "      <td>43.30</td>\n",
              "      <td>41.43</td>\n",
              "      <td>42.02</td>\n",
              "      <td>42.02</td>\n",
              "      <td>26842500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>42.01</td>\n",
              "      <td>42.84</td>\n",
              "      <td>40.84</td>\n",
              "      <td>42.26</td>\n",
              "      <td>42.26</td>\n",
              "      <td>31309500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>42.67</td>\n",
              "      <td>42.96</td>\n",
              "      <td>41.96</td>\n",
              "      <td>42.19</td>\n",
              "      <td>42.19</td>\n",
              "      <td>14842000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>42.56</td>\n",
              "      <td>42.76</td>\n",
              "      <td>42.00</td>\n",
              "      <td>42.12</td>\n",
              "      <td>42.12</td>\n",
              "      <td>17212500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close      Volume\n",
              "0  2015-01-02  44.57  44.65  42.65  43.86       43.86  23822000.0\n",
              "1  2015-01-05  42.91  43.30  41.43  42.02       42.02  26842500.0\n",
              "2  2015-01-06  42.01  42.84  40.84  42.26       42.26  31309500.0\n",
              "3  2015-01-07  42.67  42.96  41.96  42.19       42.19  14842000.0\n",
              "4  2015-01-08  42.56  42.76  42.00  42.12       42.12  17212500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdjJ0wBzoWnN",
        "outputId": "0d5cefce-0574-463f-fb00-7e2c9e0bf8be"
      },
      "source": [
        "TSLA.info()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CsNqpeBoX4q"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "TSLA[\"Date\"] = pd.to_datetime(TSLA[\"Date\"])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JXe11gDogs1",
        "outputId": "dd1555b4-fd23-452c-ebd9-359ec70373c4"
      },
      "source": [
        "Test_Stationarity(TSLA)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey Fuller Test\n",
            "Test Statistics: 0.3998006273481104\n",
            "p-value: 0.9814874842817937\n",
            "Number of lags used: 24\n",
            "Number of observations used: 1524\n",
            "critical value (1%): -3.4346481290263013\n",
            "critical value (5%): -2.8634383565975736\n",
            "critical value (10%): -2.567780658251872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aM_Ue90lKXtA"
      },
      "source": [
        "The p-value > 0.05, so we cannot reject the Null hypothesis. Hence, we would need to use the “Integrated (I)” concept, denoted by value ‘d’ in time series, to make the data stationary while building the Auto ARIMA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JllEmx6eLnK5"
      },
      "source": [
        "Now let's take log of the 'Adj. Close' column to reduce the magnitude of the values and reduce the series rising trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqQxW1vlLYLW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "93969dd6-57b2-451e-a282-887ac23cad95"
      },
      "source": [
        "TSLA['log Adj. Close'] = np.log(TSLA['Adj. Close'])\r\n",
        "TSLA_log_moving_avg = TSLA['log Adj. Close'].rolling(12).mean()\r\n",
        "TSLA_log_std = TSLA['log Adj. Close'].rolling(12).std()\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(TSLA['Date'], TSLA_log_moving_avg, label = \"Rolling Mean\")\r\n",
        "plt.plot(TSLA['Date'], TSLA_log_std, label = \"Rolling Std\")\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')\r\n",
        "plt.title(\"Rolling Mean and Standard Deviation\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rolling Mean and Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-a_pxvtxMPpd"
      },
      "source": [
        "###Split the data into training and test set\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "wR4ZkaR_olHJ",
        "outputId": "dceddd14-f6a3-4c69-83ec-8cf1593fce22"
      },
      "source": [
        "TSLA_Train_Data = TSLA[TSLA['Date'] < '2020-10-01']\r\n",
        "TSLA_Test_Data = TSLA[TSLA['Date'] >= '2020-10-01'].reset_index(drop = True)\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(TSLA_Train_Data['Date'], TSLA_Train_Data['log Adj. Close'], label = 'Train Data')\r\n",
        "plt.plot(TSLA_Test_Data['Date'], TSLA_Test_Data['log Adj. Close'], label = 'Test Data')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff6199eb1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wChwSDH-ReXi"
      },
      "source": [
        "###Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1RapLyyepXKT",
        "outputId": "7771746f-23b7-47ac-8428-73efc48f7a19"
      },
      "source": [
        "TSLA_Auto_ARIMA_Model = auto_arima(TSLA_Train_Data['log Adj. Close'], seasonal = False,\r\n",
        "                                   error_action = 'ignore', suppress_warnings = True)\r\n",
        "print(TSLA_Auto_ARIMA_Model.summary())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 1447\n",
            "Model:               SARIMAX(5, 2, 2)   Log Likelihood                2803.438\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -5588.875\n",
            "Time:                        09:13:45   BIC                          -5541.392\n",
            "Sample:                             0   HQIC                         -5571.153\n",
            "                               - 1447                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept   8.908e-06   5.33e-05      0.167      0.867   -9.56e-05       0.000\n",
            "ar.L1         -0.6933      7.704     -0.090      0.928     -15.793      14.407\n",
            "ar.L2          0.0633      0.247      0.256      0.798      -0.421       0.547\n",
            "ar.L3          0.0853      0.310      0.275      0.783      -0.523       0.693\n",
            "ar.L4         -0.0010      0.432     -0.002      0.998      -0.848       0.846\n",
            "ar.L5         -0.0311      0.311     -0.100      0.920      -0.640       0.578\n",
            "ma.L1         -0.2525      7.703     -0.033      0.974     -15.350      14.845\n",
            "ma.L2         -0.7085      7.530     -0.094      0.925     -15.467      14.050\n",
            "sigma2         0.0012   2.42e-05     49.787      0.000       0.001       0.001\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   1.95   Jarque-Bera (JB):              2838.39\n",
            "Prob(Q):                              0.16   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               3.40   Skew:                            -0.31\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.84\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfLnB9BCp3qc",
        "outputId": "2a872cba-4a77-4656-b270-b3f020167d1f"
      },
      "source": [
        "TSLA_ARIMA_Model = ARIMA(TSLA_Train_Data['log Adj. Close'], order = (5, 2, 2))\r\n",
        "TSLA_ARIMA_Model_Fit = TSLA_ARIMA_Model.fit()\r\n",
        "print(TSLA_ARIMA_Model_Fit.summary())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:      D2.log Adj. Close   No. Observations:                 1445\n",
            "Model:                 ARIMA(5, 2, 2)   Log Likelihood                2811.906\n",
            "Method:                       css-mle   S.D. of innovations              0.034\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -5605.812\n",
            "Time:                        09:13:48   BIC                          -5558.329\n",
            "Sample:                             2   HQIC                         -5588.089\n",
            "                                                                              \n",
            "===========================================================================================\n",
            "                              coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "const                    4.701e-06   3.63e-06      1.295      0.195   -2.41e-06    1.18e-05\n",
            "ar.L1.D2.log Adj. Close    -0.7382      1.030     -0.717      0.474      -2.757       1.281\n",
            "ar.L2.D2.log Adj. Close     0.0493      0.034      1.444      0.149      -0.018       0.116\n",
            "ar.L3.D2.log Adj. Close     0.0780      0.059      1.325      0.185      -0.037       0.193\n",
            "ar.L4.D2.log Adj. Close    -0.0088      0.054     -0.162      0.871      -0.115       0.097\n",
            "ar.L5.D2.log Adj. Close    -0.0411      0.028     -1.473      0.141      -0.096       0.014\n",
            "ma.L1.D2.log Adj. Close    -0.2564      1.029     -0.249      0.803      -2.273       1.761\n",
            "ma.L2.D2.log Adj. Close    -0.7409      1.021     -0.725      0.468      -2.743       1.261\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1           -1.3011           -0.0000j            1.3011           -0.5000\n",
            "AR.2           -1.2725           -1.4429j            1.9238           -0.3650\n",
            "AR.3           -1.2725           +1.4429j            1.9238            0.3650\n",
            "AR.4            1.8162           -1.3267j            2.2492           -0.1004\n",
            "AR.5            1.8162           +1.3267j            2.2492            0.1004\n",
            "MA.1            1.0015           +0.0000j            1.0015            0.0000\n",
            "MA.2           -1.3477           +0.0000j            1.3477            0.5000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVsGGJkeM-A9"
      },
      "source": [
        "###Predicting the closing stock price of Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "DFySYonSrhQb",
        "outputId": "70ea4a63-2581-46de-c6e7-3d458b67cd65"
      },
      "source": [
        "TSLA_output = TSLA_ARIMA_Model_Fit.forecast(102, alpha=0.05)\r\n",
        "TSLA_predictions = np.exp(TSLA_output[0])\r\n",
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(TSLA_Train_Data['Date'], TSLA_Train_Data['Adj. Close'], label = 'Training')\r\n",
        "plt.plot(TSLA_Test_Data['Date'], TSLA_Test_Data['Adj. Close'], label = 'Testing')\r\n",
        "plt.plot(TSLA_Test_Data['Date'], TSLA_predictions, label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff619928250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SX7T-8JPr1vd",
        "outputId": "5119376d-f898-44e9-9633-96a0e7709fe9"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(TSLA_Test_Data['Adj. Close'], TSLA_predictions))\r\n",
        "mape = np.mean(np.abs(TSLA_predictions - TSLA_Test_Data['Adj. Close'])/np.abs(TSLA_Test_Data['Adj. Close']))\r\n",
        "\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 108.11371680798878\n",
            "MAPE: 0.1303704319549627\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvUKldXqy82G"
      },
      "source": [
        "#Google"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "xErJ0CQ3so4e",
        "outputId": "4513260e-e4cd-410f-dd58-ae5c6fc421a0"
      },
      "source": [
        "GOOG.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>527.56</td>\n",
              "      <td>529.82</td>\n",
              "      <td>522.67</td>\n",
              "      <td>523.37</td>\n",
              "      <td>523.37</td>\n",
              "      <td>1447563.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>521.83</td>\n",
              "      <td>522.89</td>\n",
              "      <td>511.66</td>\n",
              "      <td>512.46</td>\n",
              "      <td>512.46</td>\n",
              "      <td>2059840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>513.59</td>\n",
              "      <td>514.76</td>\n",
              "      <td>499.68</td>\n",
              "      <td>500.59</td>\n",
              "      <td>500.59</td>\n",
              "      <td>2899940.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>505.61</td>\n",
              "      <td>505.86</td>\n",
              "      <td>498.28</td>\n",
              "      <td>499.73</td>\n",
              "      <td>499.73</td>\n",
              "      <td>2065054.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>496.63</td>\n",
              "      <td>502.10</td>\n",
              "      <td>489.66</td>\n",
              "      <td>501.30</td>\n",
              "      <td>501.30</td>\n",
              "      <td>3353582.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open    High     Low   Close  Adj. Close     Volume\n",
              "0  2015-01-02  527.56  529.82  522.67  523.37      523.37  1447563.0\n",
              "1  2015-01-05  521.83  522.89  511.66  512.46      512.46  2059840.0\n",
              "2  2015-01-06  513.59  514.76  499.68  500.59      500.59  2899940.0\n",
              "3  2015-01-07  505.61  505.86  498.28  499.73      499.73  2065054.0\n",
              "4  2015-01-08  496.63  502.10  489.66  501.30      501.30  3353582.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVZhlkydsqJb",
        "outputId": "22823c90-c486-46c7-f01f-2ff7c63fec1a"
      },
      "source": [
        "GOOG.info()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yikWnnrsr9f"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "GOOG[\"Date\"] = pd.to_datetime(GOOG[\"Date\"])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRMrtOvKsy42",
        "outputId": "fbfdb87d-a038-405b-9442-63024171d208"
      },
      "source": [
        "Test_Stationarity(GOOG)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey Fuller Test\n",
            "Test Statistics: 0.6722962942047408\n",
            "p-value: 0.9892684650395426\n",
            "Number of lags used: 22\n",
            "Number of observations used: 1526\n",
            "critical value (1%): -3.4346424863111396\n",
            "critical value (5%): -2.8634358661653803\n",
            "critical value (10%): -2.5677793320839823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpYy3ZVxKdBH"
      },
      "source": [
        "The p-value > 0.05, so we cannot reject the Null hypothesis. Hence, we would need to use the “Integrated (I)” concept, denoted by value ‘d’ in time series, to make the data stationary while building the Auto ARIMA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eviopHmALqz8"
      },
      "source": [
        "Now let's take log of the 'Adj. Close' column to reduce the magnitude of the values and reduce the series rising trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "RuVhHWR7s33g",
        "outputId": "ed290807-dcd9-40a1-f040-b2b79d31e5fd"
      },
      "source": [
        "GOOG['log Adj. Close'] = np.log(GOOG['Adj. Close'])\r\n",
        "GOOG_log_moving_avg = GOOG['log Adj. Close'].rolling(12).mean()\r\n",
        "GOOG_log_std = GOOG['log Adj. Close'].rolling(12).std()\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(GOOG['Date'], GOOG_log_moving_avg, label = \"Rolling Mean\")\r\n",
        "plt.plot(GOOG['Date'], GOOG_log_std, label = \"Rolling Std\")\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')\r\n",
        "plt.title(\"Rolling Mean and Standard Deviation\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rolling Mean and Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8NdBiBIMU40"
      },
      "source": [
        "###Split the data into training and test set\r\n",
        "    Training Period: 2015-01-02 - 2020-10-30\r\n",
        "\r\n",
        "    Testing Period:  2020-11-02 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "PILs-RoCtMwh",
        "outputId": "d5237ddb-46a3-47db-ccef-2f90e387e394"
      },
      "source": [
        "GOOG_Train_Data = GOOG[GOOG['Date'] < '2020-11-01']\r\n",
        "GOOG_Test_Data = GOOG[GOOG['Date'] >= '2020-11-01'].reset_index(drop = True)\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(GOOG_Train_Data['Date'], GOOG_Train_Data['log Adj. Close'], label = 'Train Data')\r\n",
        "plt.plot(GOOG_Test_Data['Date'], GOOG_Test_Data['log Adj. Close'], label = 'Test Data')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff61903ffd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8Iy1lORRhN5"
      },
      "source": [
        "###Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlUNaxD8tfug",
        "outputId": "1fe93d92-0543-4dbd-9dcc-a1c07ec53719"
      },
      "source": [
        "GOOG_Auto_ARIMA_Model = auto_arima(GOOG_Train_Data['log Adj. Close'], seasonal = False,\r\n",
        "                                   error_action = 'ignore', suppress_warnings = True)\r\n",
        "print(GOOG_Auto_ARIMA_Model.summary())"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 1469\n",
            "Model:               SARIMAX(1, 1, 0)   Log Likelihood                3905.939\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7805.878\n",
            "Time:                        09:13:56   BIC                          -7790.003\n",
            "Sample:                             0   HQIC                         -7799.958\n",
            "                               - 1469                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.0008      0.000      1.882      0.060   -3.47e-05       0.002\n",
            "ar.L1         -0.0876      0.015     -5.798      0.000      -0.117      -0.058\n",
            "sigma2         0.0003   4.48e-06     63.886      0.000       0.000       0.000\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              5179.36\n",
            "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               1.63   Skew:                             0.19\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                        12.19\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZEjonRRtwpd",
        "outputId": "b6f42fa9-e745-43ec-92da-ffc13c6c0d29"
      },
      "source": [
        "GOOG_ARIMA_Model = ARIMA(GOOG_Train_Data['log Adj. Close'], order = (1, 1, 0))\r\n",
        "GOOG_ARIMA_Model_Fit = GOOG_ARIMA_Model.fit()\r\n",
        "print(GOOG_ARIMA_Model_Fit.summary())"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:       D.log Adj. Close   No. Observations:                 1468\n",
            "Model:                 ARIMA(1, 1, 0)   Log Likelihood                3905.939\n",
            "Method:                       css-mle   S.D. of innovations              0.017\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7805.879\n",
            "Time:                        09:13:56   BIC                          -7790.004\n",
            "Sample:                             1   HQIC                         -7799.958\n",
            "                                                                              \n",
            "==========================================================================================\n",
            "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------------\n",
            "const                      0.0008      0.000      1.896      0.058    -2.6e-05       0.002\n",
            "ar.L1.D.log Adj. Close    -0.0877      0.026     -3.367      0.001      -0.139      -0.037\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1          -11.4059           +0.0000j           11.4059            0.5000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qacgTwHNNBtt"
      },
      "source": [
        "###Predicting the closing stock price of Google"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "V-PcSO-at-qT",
        "outputId": "d4545453-fe45-4f8e-bfdc-0bad7e1c4b3e"
      },
      "source": [
        "GOOG_output = GOOG_ARIMA_Model_Fit.forecast(80, alpha=0.05)\r\n",
        "GOOG_predictions = np.exp(GOOG_output[0])\r\n",
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(GOOG_Train_Data['Date'], GOOG_Train_Data['Adj. Close'], label = 'Training')\r\n",
        "plt.plot(GOOG_Test_Data['Date'], GOOG_Test_Data['Adj. Close'], label = 'Testing')\r\n",
        "plt.plot(GOOG_Test_Data['Date'], GOOG_predictions, label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff61900e090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc894ymcuJJW",
        "outputId": "10447a51-6d60-4825-b4b9-3fe64858adc4"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(GOOG_Test_Data['Adj. Close'], GOOG_predictions))\r\n",
        "mape = np.mean(np.abs(GOOG_predictions - GOOG_Test_Data['Adj. Close'])/np.abs(GOOG_Test_Data['Adj. Close']))\r\n",
        "\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 210.40237555557943\n",
            "MAPE: 0.09222702265794448\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_W5QhHlzCmI"
      },
      "source": [
        "#Microsoft"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "S9u6WSEUunoE",
        "outputId": "2c86580f-aa4c-4a83-e208-22189d28d978"
      },
      "source": [
        "MSFT.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>46.66</td>\n",
              "      <td>47.42</td>\n",
              "      <td>46.54</td>\n",
              "      <td>46.76</td>\n",
              "      <td>41.44</td>\n",
              "      <td>27913900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>46.37</td>\n",
              "      <td>46.73</td>\n",
              "      <td>46.25</td>\n",
              "      <td>46.33</td>\n",
              "      <td>41.06</td>\n",
              "      <td>39673900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>46.38</td>\n",
              "      <td>46.75</td>\n",
              "      <td>45.54</td>\n",
              "      <td>45.65</td>\n",
              "      <td>40.46</td>\n",
              "      <td>36447900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>45.98</td>\n",
              "      <td>46.46</td>\n",
              "      <td>45.49</td>\n",
              "      <td>46.23</td>\n",
              "      <td>40.97</td>\n",
              "      <td>29114100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>46.75</td>\n",
              "      <td>47.75</td>\n",
              "      <td>46.72</td>\n",
              "      <td>47.59</td>\n",
              "      <td>42.18</td>\n",
              "      <td>29645200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj. Close      Volume\n",
              "0  2015-01-02  46.66  47.42  46.54  46.76       41.44  27913900.0\n",
              "1  2015-01-05  46.37  46.73  46.25  46.33       41.06  39673900.0\n",
              "2  2015-01-06  46.38  46.75  45.54  45.65       40.46  36447900.0\n",
              "3  2015-01-07  45.98  46.46  45.49  46.23       40.97  29114100.0\n",
              "4  2015-01-08  46.75  47.75  46.72  47.59       42.18  29645200.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRKz1iCtupHM",
        "outputId": "2242e99a-b388-4ca4-807b-f7d077c286c3"
      },
      "source": [
        "MSFT.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kD4cy5Laurga"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "MSFT[\"Date\"] = pd.to_datetime(MSFT[\"Date\"])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6VHqbXkuypI",
        "outputId": "87c86f2e-a252-4b25-a991-cd430846b1c9"
      },
      "source": [
        "Test_Stationarity(MSFT)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey Fuller Test\n",
            "Test Statistics: 2.1271168914605765\n",
            "p-value: 0.9988169756931882\n",
            "Number of lags used: 24\n",
            "Number of observations used: 1524\n",
            "critical value (1%): -3.4346481290263013\n",
            "critical value (5%): -2.8634383565975736\n",
            "critical value (10%): -2.567780658251872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhhaesjtKf2B"
      },
      "source": [
        "The p-value > 0.05, so we cannot reject the Null hypothesis. Hence, we would need to use the “Integrated (I)” concept, denoted by value ‘d’ in time series, to make the data stationary while building the Auto ARIMA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-7f4tw3Luoc"
      },
      "source": [
        "Now let's take log of the 'Adj. Close' column to reduce the magnitude of the values and reduce the series rising trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "PDUMAlDvu3xG",
        "outputId": "fe1e4944-ae92-4a36-f929-db26369c1a88"
      },
      "source": [
        "MSFT['log Adj. Close'] = np.log(MSFT['Adj. Close'])\r\n",
        "MSFT_log_moving_avg = MSFT['log Adj. Close'].rolling(12).mean()\r\n",
        "MSFT_log_std = MSFT['log Adj. Close'].rolling(12).std()\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(MSFT['Date'], MSFT_log_moving_avg, label = \"Rolling Mean\")\r\n",
        "plt.plot(MSFT['Date'], MSFT_log_std, label = \"Rolling Std\")\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')\r\n",
        "plt.title(\"Rolling Mean and Standard Deviation\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rolling Mean and Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isAR11JHMYWV"
      },
      "source": [
        "###Split the data into training and test set\r\n",
        "    Training Period: 2015-01-02 - 2020-09-30\r\n",
        "\r\n",
        "    Testing Period:  2020-10-01 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "vuyO02PHvLdl",
        "outputId": "d2f2265e-6533-453b-83e1-7d6daa532c00"
      },
      "source": [
        "MSFT_Train_Data = MSFT[MSFT['Date'] < '2020-10-01']\r\n",
        "MSFT_Test_Data = MSFT[MSFT['Date'] >= '2020-10-01'].reset_index(drop = True)\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(MSFT_Train_Data['Date'], MSFT_Train_Data['log Adj. Close'], label = 'Train Data')\r\n",
        "plt.plot(MSFT_Test_Data['Date'], MSFT_Test_Data['log Adj. Close'], label = 'Test Data')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff619c81dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "psRZu2CDRlP3"
      },
      "source": [
        "###Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gg7RggbCvcPN",
        "outputId": "b89d0cf7-5162-48c3-8e46-90dcb428f158"
      },
      "source": [
        "MSFT_Auto_ARIMA_Model = auto_arima(MSFT_Train_Data['log Adj. Close'], seasonal = False,\r\n",
        "                                   error_action = 'ignore', suppress_warnings = True)\r\n",
        "print(MSFT_Auto_ARIMA_Model.summary())"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 1447\n",
            "Model:               SARIMAX(1, 1, 0)   Log Likelihood                3828.056\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7650.112\n",
            "Time:                        09:14:01   BIC                          -7634.282\n",
            "Sample:                             0   HQIC                         -7644.204\n",
            "                               - 1447                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.0014      0.000      2.995      0.003       0.000       0.002\n",
            "ar.L1         -0.2169      0.012    -18.786      0.000      -0.239      -0.194\n",
            "sigma2         0.0003   5.01e-06     58.650      0.000       0.000       0.000\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):              4743.33\n",
            "Prob(Q):                              0.93   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               1.58   Skew:                            -0.35\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.85\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPLIuK-CvqJy",
        "outputId": "d99230ae-d46b-476b-dd2f-18d19f503788"
      },
      "source": [
        "MSFT_ARIMA_Model = ARIMA(MSFT_Train_Data['log Adj. Close'], order = (1, 1, 0))\r\n",
        "MSFT_ARIMA_Model_Fit = MSFT_ARIMA_Model.fit()\r\n",
        "print(MSFT_ARIMA_Model_Fit.summary())"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:       D.log Adj. Close   No. Observations:                 1446\n",
            "Model:                 ARIMA(1, 1, 0)   Log Likelihood                3828.056\n",
            "Method:                       css-mle   S.D. of innovations              0.017\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7650.112\n",
            "Time:                        09:14:01   BIC                          -7634.283\n",
            "Sample:                             1   HQIC                         -7644.204\n",
            "                                                                              \n",
            "==========================================================================================\n",
            "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------------\n",
            "const                      0.0011      0.000      3.022      0.003       0.000       0.002\n",
            "ar.L1.D.log Adj. Close    -0.2168      0.026     -8.445      0.000      -0.267      -0.166\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1           -4.6131           +0.0000j            4.6131            0.5000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_45tUmFNFWt"
      },
      "source": [
        "###Predicting the closing stock price of Microsoft"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "sAKpYMxXvybd",
        "outputId": "1666fba7-4809-4154-d793-a16aeb93a42b"
      },
      "source": [
        "MSFT_output = MSFT_ARIMA_Model_Fit.forecast(102, alpha=0.05)\r\n",
        "MSFT_predictions = np.exp(MSFT_output[0])\r\n",
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(MSFT_Train_Data['Date'], MSFT_Train_Data['Adj. Close'], label = 'Training')\r\n",
        "plt.plot(MSFT_Test_Data['Date'], MSFT_Test_Data['Adj. Close'], label = 'Testing')\r\n",
        "plt.plot(MSFT_Test_Data['Date'], MSFT_predictions, label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff618fb8190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9b1MDWjNv-Aj",
        "outputId": "790d4b91-d085-4b3d-fbcc-ba23e46bc247"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(MSFT_Test_Data['Adj. Close'], MSFT_predictions))\r\n",
        "mape = np.mean(np.abs(MSFT_predictions - MSFT_Test_Data['Adj. Close'])/np.abs(MSFT_Test_Data['Adj. Close']))\r\n",
        "\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 7.269511877014612\n",
            "MAPE: 0.027081038152935593\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2PZvGA2zLPq"
      },
      "source": [
        "#Amazon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "YFUiwuB8wQcT",
        "outputId": "ca5f7906-326c-425b-df92-17ecc97aae0f"
      },
      "source": [
        "AMZN.head()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>312.58</td>\n",
              "      <td>314.75</td>\n",
              "      <td>306.96</td>\n",
              "      <td>308.52</td>\n",
              "      <td>308.52</td>\n",
              "      <td>2783200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>307.01</td>\n",
              "      <td>308.38</td>\n",
              "      <td>300.85</td>\n",
              "      <td>302.19</td>\n",
              "      <td>302.19</td>\n",
              "      <td>2774200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>302.24</td>\n",
              "      <td>303.00</td>\n",
              "      <td>292.38</td>\n",
              "      <td>295.29</td>\n",
              "      <td>295.29</td>\n",
              "      <td>3519000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>297.50</td>\n",
              "      <td>301.28</td>\n",
              "      <td>295.33</td>\n",
              "      <td>298.42</td>\n",
              "      <td>298.42</td>\n",
              "      <td>2640300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>300.32</td>\n",
              "      <td>303.14</td>\n",
              "      <td>296.11</td>\n",
              "      <td>300.46</td>\n",
              "      <td>300.46</td>\n",
              "      <td>3088400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open    High     Low   Close  Adj. Close     Volume\n",
              "0  2015-01-02  312.58  314.75  306.96  308.52      308.52  2783200.0\n",
              "1  2015-01-05  307.01  308.38  300.85  302.19      302.19  2774200.0\n",
              "2  2015-01-06  302.24  303.00  292.38  295.29      295.29  3519000.0\n",
              "3  2015-01-07  297.50  301.28  295.33  298.42      298.42  2640300.0\n",
              "4  2015-01-08  300.32  303.14  296.11  300.46      300.46  3088400.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZw66OsHwYSX",
        "outputId": "bccc89be-8bb4-4492-c96a-48eef0c8b6c8"
      },
      "source": [
        "AMZN.info()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1549 entries, 0 to 1548\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Date        1549 non-null   object \n",
            " 1   Open        1549 non-null   float64\n",
            " 2   High        1549 non-null   float64\n",
            " 3   Low         1549 non-null   float64\n",
            " 4   Close       1549 non-null   float64\n",
            " 5   Adj. Close  1549 non-null   float64\n",
            " 6   Volume      1549 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 84.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaElbNDDwZg1"
      },
      "source": [
        "# Change Dtype of Date column\r\n",
        "AMZN[\"Date\"] = pd.to_datetime(AMZN[\"Date\"])"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmSimPQ7wfHt",
        "outputId": "5a6963ee-3835-4ecb-d3ea-b29e76f65809"
      },
      "source": [
        "Test_Stationarity(AMZN)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results of Dickey Fuller Test\n",
            "Test Statistics: 0.2703409634404918\n",
            "p-value: 0.9759420712303004\n",
            "Number of lags used: 18\n",
            "Number of observations used: 1530\n",
            "critical value (1%): -3.434631245212095\n",
            "critical value (5%): -2.8634309048528066\n",
            "critical value (10%): -2.5677766901619035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TpUEFcJKi5k"
      },
      "source": [
        "The p-value > 0.05, so we cannot reject the Null hypothesis. Hence, we would need to use the “Integrated (I)” concept, denoted by value ‘d’ in time series, to make the data stationary while building the Auto ARIMA model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeSztRY0LyPV"
      },
      "source": [
        "Now let's take log of the 'Adj. Close' column to reduce the magnitude of the values and reduce the series rising trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "3i94PqZNwkgd",
        "outputId": "0a7549fd-d6bb-4bb5-87af-22e07f371814"
      },
      "source": [
        "AMZN['log Adj. Close'] = np.log(AMZN['Adj. Close'])\r\n",
        "AMZN_log_moving_avg = AMZN['log Adj. Close'].rolling(12).mean()\r\n",
        "AMZN_log_std = AMZN['log Adj. Close'].rolling(12).std()\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(AMZN['Date'], AMZN_log_moving_avg, label = \"Rolling Mean\")\r\n",
        "plt.plot(AMZN['Date'], AMZN_log_std, label = \"Rolling Std\")\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')\r\n",
        "plt.title(\"Rolling Mean and Standard Deviation\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Rolling Mean and Standard Deviation')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0K0pUngEMbo9"
      },
      "source": [
        "###Split the data into training and test set\r\n",
        "    Training Period: 2015-01-02 - 2020-10-30\r\n",
        "\r\n",
        "    Testing Period:  2020-11-02 - 2021-02-26"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "XSQ_FNdOw0z2",
        "outputId": "259e0f61-18af-45c8-8f19-949ebe7b9d09"
      },
      "source": [
        "AMZN_Train_Data = AMZN[AMZN['Date'] < '2020-11-01']\r\n",
        "AMZN_Test_Data = AMZN[AMZN['Date'] >= '2020-11-01'].reset_index(drop = True)\r\n",
        "\r\n",
        "plt.figure(figsize = (10, 5))\r\n",
        "plt.plot(AMZN_Train_Data['Date'], AMZN_Train_Data['log Adj. Close'], label = 'Train Data')\r\n",
        "plt.plot(AMZN_Test_Data['Date'], AMZN_Test_Data['log Adj. Close'], label = 'Test Data')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('log Adj. Close')\r\n",
        "plt.legend(loc = 'best')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff618ced7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Qm9bCGQRoxZ"
      },
      "source": [
        "###Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "doJ6f30axHK_",
        "outputId": "5f7083a8-bcec-4257-bb70-cfeb9834f411"
      },
      "source": [
        "AMZN_Auto_ARIMA_Model = auto_arima(AMZN_Train_Data['log Adj. Close'], seasonal = False,\r\n",
        "                                   error_action = 'ignore', suppress_warnings = True)\r\n",
        "print(AMZN_Auto_ARIMA_Model.summary())"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 1469\n",
            "Model:               SARIMAX(0, 1, 0)   Log Likelihood                3704.612\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7405.225\n",
            "Time:                        09:14:06   BIC                          -7394.641\n",
            "Sample:                             0   HQIC                         -7401.278\n",
            "                               - 1469                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.0016      0.001      3.025      0.002       0.001       0.003\n",
            "sigma2         0.0004   6.92e-06     54.419      0.000       0.000       0.000\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   1.40   Jarque-Bera (JB):              2529.69\n",
            "Prob(Q):                              0.24   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               1.14   Skew:                             0.52\n",
            "Prob(H) (two-sided):                  0.15   Kurtosis:                         9.34\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orJs2sk7xY1s",
        "outputId": "c09871ca-37da-4c83-e659-edc82083e94f"
      },
      "source": [
        "AMZN_ARIMA_Model = ARIMA(AMZN_Train_Data['log Adj. Close'], order = (0, 1, 0))\r\n",
        "AMZN_ARIMA_Model_Fit = AMZN_ARIMA_Model.fit()\r\n",
        "print(AMZN_ARIMA_Model_Fit.summary())"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:       D.log Adj. Close   No. Observations:                 1468\n",
            "Model:                 ARIMA(0, 1, 0)   Log Likelihood                3704.612\n",
            "Method:                           css   S.D. of innovations              0.019\n",
            "Date:                Wed, 03 Mar 2021   AIC                          -7405.225\n",
            "Time:                        09:14:06   BIC                          -7394.641\n",
            "Sample:                             1   HQIC                         -7401.278\n",
            "                                                                              \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          0.0016      0.001      3.076      0.002       0.001       0.003\n",
            "==============================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW8Hh1mENILx"
      },
      "source": [
        "###Predicting the closing stock price of Amazon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "8ApmgHP-xhQX",
        "outputId": "5bb3d842-7bd2-4e1b-a123-335c9e4d5719"
      },
      "source": [
        "AMZN_output = AMZN_ARIMA_Model_Fit.forecast(80, alpha=0.05)\r\n",
        "AMZN_predictions = np.exp(AMZN_output[0])\r\n",
        "plt.figure(figsize=(10, 5))\r\n",
        "plt.plot(AMZN_Train_Data['Date'], AMZN_Train_Data['Adj. Close'], label = 'Training')\r\n",
        "plt.plot(AMZN_Test_Data['Date'], AMZN_Test_Data['Adj. Close'], label = 'Testing')\r\n",
        "plt.plot(AMZN_Test_Data['Date'], AMZN_predictions, label = 'Predictions')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Closing Price')\r\n",
        "plt.legend()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff618c34dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86FDrtlTxuVH",
        "outputId": "a1115ce1-bafd-4bd1-f7a5-9dc630bb1e42"
      },
      "source": [
        "rmse = math.sqrt(mean_squared_error(AMZN_Test_Data['Adj. Close'], AMZN_predictions))\r\n",
        "mape = np.mean(np.abs(AMZN_predictions - AMZN_Test_Data['Adj. Close'])/np.abs(AMZN_Test_Data['Adj. Close']))\r\n",
        "\r\n",
        "print(f'RMSE: {rmse}')\r\n",
        "print(f'MAPE: {mape}')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 113.18558052462348\n",
            "MAPE: 0.02500054538949603\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}