Stationary_Check_Part_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Check if Time Series Stationary"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:40.932Z",
          "iopub.execute_input": "2021-06-23T01:51:40.936Z",
          "iopub.status.idle": "2021-06-23T01:51:42.042Z",
          "shell.execute_reply": "2021-06-23T01:51:42.056Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "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>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    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:42.048Z",
          "iopub.execute_input": "2021-06-23T01:51:42.052Z",
          "iopub.status.idle": "2021-06-23T01:51:43.008Z",
          "shell.execute_reply": "2021-06-23T01:51:43.052Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "series = dataset['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.014Z",
          "iopub.execute_input": "2021-06-23T01:51:43.019Z",
          "iopub.status.idle": "2021-06-23T01:51:43.027Z",
          "shell.execute_reply": "2021-06-23T01:51:43.056Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "series.plot()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.033Z",
          "iopub.execute_input": "2021-06-23T01:51:43.037Z",
          "iopub.status.idle": "2021-06-23T01:51:43.147Z",
          "shell.execute_reply": "2021-06-23T01:51:43.169Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "series.hist()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.156Z",
          "iopub.execute_input": "2021-06-23T01:51:43.161Z",
          "shell.execute_reply": "2021-06-23T01:51:43.275Z",
          "iopub.status.idle": "2021-06-23T01:51:43.241Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = series.values\n",
        "split = round(len(X) / 2)\n",
        "X1, X2 = X[0:split], X[split:]\n",
        "mean1, mean2 = X1.mean(), X2.mean()\n",
        "var1, var2 = X1.var(), X2.var()\n",
        "print('mean1=%f, mean2=%f' % (mean1, mean2))\n",
        "print('variance1=%f, variance2=%f' % (var1, var2))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean1=3.017122, mean2=13.311733\n",
            "variance1=0.766209, variance2=30.028234\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.247Z",
          "iopub.execute_input": "2021-06-23T01:51:43.250Z",
          "iopub.status.idle": "2021-06-23T01:51:43.260Z",
          "shell.execute_reply": "2021-06-23T01:51:43.278Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = series.values\n",
        "X = np.log(X)\n",
        "plt.hist(X)\n",
        "plt.show()\n",
        "plt.plot(X)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.265Z",
          "iopub.execute_input": "2021-06-23T01:51:43.271Z",
          "iopub.status.idle": "2021-06-23T01:51:43.429Z",
          "shell.execute_reply": "2021-06-23T01:51:43.423Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hypothesis Testing\n",
        "\n",
        "#### Null Hypothesis (H0): If failed to be rejected, it suggests the time series has a unit root, meaning it is non-stationary. It has some time dependent structure.\n",
        "\n",
        "#### Alternate Hypothesis (H1): The null hypothesis is rejected; it suggests the time series does not have a unit root, meaning it is stationary. It does not have time-dependent structure.\n"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### p-value > 0.05: Fail to reject the null hypothesis (H0), the data has a unit root and is non-stationary.\n",
        "\n",
        "#### p-value <= 0.05: Reject the null hypothesis (H0), the data does not have a unit root and is stationary.\n"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = adfuller(X)\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -0.349428\n",
            "p-value: 0.918206\n",
            "Critical Values:\n",
            "\t1%: -3.436\n",
            "\t5%: -2.864\n",
            "\t10%: -2.568\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-06-23T01:51:43.434Z",
          "iopub.execute_input": "2021-06-23T01:51:43.441Z",
          "iopub.status.idle": "2021-06-23T01:51:43.470Z",
          "shell.execute_reply": "2021-06-23T01:51:43.476Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### As a result, the value is larger than the critical values, again, meaning that we can fail to reject the null hypothesis and in turn that the time series is non-stationary."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}