NSWPIPELINE.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/virajvaidya/Australia-Energy-Demand-Forecasting/blob/main/NSWPIPELINE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dEYMxyK58z12"
      },
      "outputs": [],
      "source": [
        "!pip install pandas==1.3.5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k8eKW2Hj86Yf"
      },
      "outputs": [],
      "source": [
        "!pip install plotly==5.4.0\n",
        "!pip install chart_studio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "22CDQLM287wI"
      },
      "outputs": [],
      "source": [
        "!pip install statsmodels==0.13.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AOJ4CXQy89R9"
      },
      "outputs": [],
      "source": [
        "!pip install pmdarima"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "4kq9RVjn8-rf"
      },
      "outputs": [],
      "source": [
        "import pandas       as pd\n",
        "import numpy        as np\n",
        "import pmdarima     as pm\n",
        "from pmdarima       import pipeline\n",
        "from pmdarima       import model_selection\n",
        "from pmdarima       import preprocessing as ppc\n",
        "from pmdarima       import arima\n",
        "from matplotlib     import pyplot as plt\n",
        "import seaborn      as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "AAk5b8xJ-w_M"
      },
      "outputs": [],
      "source": [
        "import plotly.graph_objs    as go\n",
        "from plotly.offline                       import iplot\n",
        "from plotly.subplots                      import make_subplots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "GwSh5fK79AfU"
      },
      "outputs": [],
      "source": [
        "sns.set_style('whitegrid')\n",
        "pd.plotting.register_matplotlib_converters()\n",
        "# Default figure size\n",
        "sns.mpl.rc('figure',figsize=(16, 6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "xcHWzCtD9CZ4",
        "outputId": "3b1dae84-776d-4ee2-9675-50e499c6a611"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1cbbfb91-c3bd-4970-8877-0d531bab2a75\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1cbbfb91-c3bd-4970-8877-0d531bab2a75\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving NSW.csv to NSW.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import io\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "uZmKuZTQ9rv9"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('NSW.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "0rJ53lur9xqZ"
      },
      "outputs": [],
      "source": [
        "df= df[['DATE','TOTALDEMANDNSW','RRPNSW']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "CkZtUduD9343"
      },
      "outputs": [],
      "source": [
        "df['DATE'] = df['DATE'].apply(lambda x: pd.Timestamp(x).strftime('%d/%m/%Y %H%M'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "IXtUeW6z-dHg"
      },
      "outputs": [],
      "source": [
        "df['DATE'] = df['DATE'].astype('datetime64[ns]')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utrEFkHa_tft",
        "outputId": "e5aba3e6-8437-4c52-c444-210edb0b369d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 70942 entries, 0 to 70941\n",
            "Data columns (total 3 columns):\n",
            " #   Column          Non-Null Count  Dtype         \n",
            "---  ------          --------------  -----         \n",
            " 0   DATE            70942 non-null  datetime64[ns]\n",
            " 1   TOTALDEMANDNSW  70942 non-null  float64       \n",
            " 2   RRPNSW          70942 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "pc81cKeY_s2h"
      },
      "outputs": [],
      "source": [
        "data = df['TOTALDEMANDNSW']\n",
        "train, test = model_selection.train_test_split(data, train_size=40000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "w9R7w_pA_0lX"
      },
      "outputs": [],
      "source": [
        "pipe = pipeline.Pipeline([\n",
        "    (\"fourier\", ppc.FourierFeaturizer(m=12, k=4)),\n",
        "    (\"arima\", arima.AutoARIMA(stepwise=True, trace=1, error_action=\"ignore\",\n",
        "                              information_criterion='bic',\n",
        "                              seasonal=False,  # because we use Fourier\n",
        "                              suppress_warnings=True))\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "828LhugU_5YC",
        "outputId": "65446980-0bf4-4be2-f639-ca553d0184ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize bic\n",
            " ARIMA(2,1,2)(0,0,0)[0] intercept   : BIC=486626.239, Time=99.28 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0] intercept   : BIC=540991.806, Time=3.44 sec\n",
            " ARIMA(1,1,0)(0,0,0)[0] intercept   : BIC=491458.080, Time=8.46 sec\n",
            " ARIMA(0,1,1)(0,0,0)[0] intercept   : BIC=514499.991, Time=32.07 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0]             : BIC=540981.538, Time=28.09 sec\n",
            " ARIMA(1,1,2)(0,0,0)[0] intercept   : BIC=489251.932, Time=52.27 sec\n",
            " ARIMA(2,1,1)(0,0,0)[0] intercept   : BIC=491411.569, Time=61.01 sec\n",
            " ARIMA(3,1,2)(0,0,0)[0] intercept   : BIC=486262.890, Time=103.51 sec\n",
            " ARIMA(3,1,1)(0,0,0)[0] intercept   : BIC=486563.104, Time=87.86 sec\n",
            " ARIMA(4,1,2)(0,0,0)[0] intercept   : BIC=486584.098, Time=150.41 sec\n",
            " ARIMA(3,1,3)(0,0,0)[0] intercept   : BIC=485665.566, Time=162.25 sec\n",
            " ARIMA(2,1,3)(0,0,0)[0] intercept   : BIC=485655.612, Time=100.02 sec\n",
            " ARIMA(1,1,3)(0,0,0)[0] intercept   : BIC=489025.101, Time=62.25 sec\n",
            " ARIMA(2,1,4)(0,0,0)[0] intercept   : BIC=485665.504, Time=122.37 sec\n",
            " ARIMA(1,1,4)(0,0,0)[0] intercept   : BIC=488955.961, Time=81.09 sec\n",
            " ARIMA(3,1,4)(0,0,0)[0] intercept   : BIC=485536.549, Time=186.13 sec\n",
            " ARIMA(4,1,4)(0,0,0)[0] intercept   : BIC=485866.380, Time=196.09 sec\n",
            " ARIMA(3,1,5)(0,0,0)[0] intercept   : BIC=485506.400, Time=183.34 sec\n",
            " ARIMA(2,1,5)(0,0,0)[0] intercept   : BIC=485673.318, Time=183.52 sec\n",
            " ARIMA(4,1,5)(0,0,0)[0] intercept   : BIC=484931.273, Time=229.78 sec\n",
            " ARIMA(5,1,5)(0,0,0)[0] intercept   : BIC=485506.163, Time=214.39 sec\n",
            " ARIMA(5,1,4)(0,0,0)[0] intercept   : BIC=485422.884, Time=243.92 sec\n",
            " ARIMA(4,1,5)(0,0,0)[0]             : BIC=inf, Time=220.20 sec\n",
            "\n",
            "Best model:  ARIMA(4,1,5)(0,0,0)[0] intercept\n",
            "Total fit time: 2811.863 seconds\n",
            "Model fit:\n",
            "Pipeline(steps=[('fourier', FourierFeaturizer(k=4, m=12)),\n",
            "                ('arima',\n",
            "                 AutoARIMA(error_action='ignore', information_criterion='bic',\n",
            "                           seasonal=False, trace=1))])\n"
          ]
        }
      ],
      "source": [
        "pipe.fit(train)\n",
        "print(\"Model fit:\")\n",
        "print(pipe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "IE9CESwVEc9q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55e35a20-7abd-4045-bfa2-428f0154ffc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Forecasts:\n",
            "[6940.73256638 6640.4846873  6391.1577404  6230.05717799 6131.93936183\n",
            " 6091.70742525 6125.39659436 6171.34192515 6231.69247537 6262.16975178\n",
            " 6305.0067797  6338.75219304 6385.80935608 6456.00761752 6544.49396028\n",
            " 6679.40900513 6809.18271819 6964.73633549 7110.94724017 7242.95988239\n",
            " 7315.85019892 7329.64500445 7311.16967751 7247.43094911 7187.046497\n",
            " 7111.46944203 7070.27183117 7046.94472867 7043.61935295 7059.37893667\n",
            " 7084.19418913 7114.46593506 7090.50232024 7046.61179794 6966.09090735\n",
            " 6883.46769304 6801.42739092 6734.70919853 6710.69877833 6712.89669619\n",
            " 6756.17904284 6806.57262937 6892.26679639 6962.46397538 6997.37119402\n",
            " 6996.3491125  6960.40211112 6920.03353961 6863.27199749 6832.38015829\n",
            " 6816.67322976 6841.57837828 6882.81791527 6938.21274052 7018.17746212\n",
            " 7075.87420022 7104.79648152 7079.38574665 7036.70769968 6969.04692\n",
            " 6902.27286097 6849.80180393 6819.22883812 6833.01649248 6856.08892424\n",
            " 6910.25986202 6973.3983384  7035.14118793 7054.11034679 7032.47385952\n",
            " 6990.11519951 6922.69328029 6865.75026745 6810.50651054 6794.54614152\n",
            " 6805.97934361 6842.07992085 6898.0675737  6967.81832576 7036.96552869\n",
            " 7055.08125606 7044.3712766  6996.54809563 6939.03965993 6876.80393263\n",
            " 6825.98701216 6808.74102133 6817.24344462 6856.83162553 6904.33287778\n",
            " 6979.82995319 7039.41066984 7061.45502372 7044.78158845 6995.99763916\n",
            " 6938.51282371 6870.24933903 6824.39074746 6798.90094358 6813.62941211]\n"
          ]
        }
      ],
      "source": [
        "preds, conf_int = pipe.predict(n_periods=100, return_conf_int=True)\n",
        "print(\"\\nForecasts:\")\n",
        "print(preds)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "NSWPIPELINE.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOW5x1YfcyCljx8ItVOy1ls",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}