ARIMA_Ukdale.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "ARIMA_Ukdale.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RachitBansal/AppliancePower_TimeSeries/blob/master/ARIMA_Ukdale.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHX5394t8gD1",
        "colab_type": "code",
        "outputId": "2b2913d2-7286-446a-fe3e-d38f0567e6db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6k-cNpec86Rq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "35d1802a-e17e-4ec9-8ed6-2d5cde5a15c8"
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrS3bezrBVb4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9cdd33e-70d3-4529-b804-88921ad0349a"
      },
      "source": [
        "eq = input(\"Enter equipment: \")\n",
        "\n",
        "train_x = np.load(file='./drive/My Drive/ukdale_'+eq+'_x.npy')\n",
        "train_y = np.load(file='./drive/My Drive/ukdale_'+eq+'_y.npy')\n",
        "test_y = np.load(file='./drive/My Drive/ukdale_'+eq+'_ty.npy')\n",
        "test_x = np.load(file='./drive/My Drive/ukdale_'+eq+'_tx.npy')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter equipment: 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsgQEByT8Ogn",
        "colab_type": "code",
        "outputId": "2ee5f6bc-dc96-4b0d-c9c6-b258b0a79fe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "from pandas import datetime\n",
        "import pandas as pd\n",
        "\n",
        "# series = joblib.load(\"hour_resampled_data.pkl\")\n",
        "# sample = series\n",
        "# sample = np.array(sample)\n",
        "# sample = sample[3000:4500,1:2]\n",
        "\n",
        "# series  = np.array(series)\n",
        "# series = series[:3000,1:2]\n",
        "# print(series.shape)\n",
        "# series = pd.DataFrame(series)\n",
        "# #series.drop(axis = \"index\")\n",
        "# print(series.head())\n",
        "# equipment = int(input('equipment: '))\n",
        "series = test_x[:3000, 0]\n",
        "\n",
        "plt.plot(series)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bcqV83N+wnZiVyb3jrTh1jNx/OSIujIjp9PMu4M6I2AbcmX62huoN0c7pB8pImULKGDqv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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynSHYGZ88Ogy",
        "colab_type": "code",
        "outputId": "b9165f6f-9981-486e-b8ab-ab15a51ab50a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "from pandas import read_csv\n",
        "from pandas import datetime\n",
        "from matplotlib import pyplot\n",
        "from pandas.plotting import autocorrelation_plot\n",
        "\n",
        "# series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
        "autocorrelation_plot(series)\n",
        "pyplot.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3PQhMzFJ1LXB7apIxsx3unnFdJ6ybRHAkNNfdn0op2wzUAPOBte5+fa+BAA0N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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3JPIPtc8Og5",
        "colab_type": "code",
        "outputId": "8c63039e-73c3-4f79-a4df-4f9193790b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from pandas import datetime\n",
        "from pandas import DataFrame\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from matplotlib import pyplot\n",
        "import numpy as np \n",
        "def parser(x):\n",
        "    return datetime.strptime('190'+x, '%Y-%m')\n",
        " \n",
        "# series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
        "# fit model\n",
        "series = np.array(series)\n",
        "model = ARIMA(series, order=(5,1,0))\n",
        "model_fit = model.fit(disp=0)\n",
        "print(model_fit.summary())\n",
        "# plot residual errors\n",
        "residuals = DataFrame(model_fit.resid)\n",
        "residuals.plot()\n",
        "pyplot.show()\n",
        "residuals.plot(kind='kde')\n",
        "pyplot.show()\n",
        "print(residuals.describe())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                    D.y   No. Observations:                 2999\n",
            "Model:                 ARIMA(5, 1, 0)   Log Likelihood              -11481.833\n",
            "Method:                       css-mle   S.D. of innovations             11.129\n",
            "Date:                Tue, 24 Dec 2019   AIC                          22977.666\n",
            "Time:                        09:37:15   BIC                          23019.708\n",
            "Sample:                             1   HQIC                         22992.789\n",
            "                                                                              \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const      -6.939e-18      0.120   -5.8e-17      1.000      -0.234       0.234\n",
            "ar.L1.D.y      0.0112      0.018      0.619      0.536      -0.024       0.047\n",
            "ar.L2.D.y     -0.3419      0.018    -18.952      0.000      -0.377      -0.307\n",
            "ar.L3.D.y     -0.1614      0.019     -8.558      0.000      -0.198      -0.124\n",
            "ar.L4.D.y     -0.0743      0.018     -4.123      0.000      -0.110      -0.039\n",
            "ar.L5.D.y     -0.1341      0.018     -7.415      0.000      -0.169      -0.099\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1            0.8802           -1.0994j            1.4084           -0.1425\n",
            "AR.2            0.8802           +1.0994j            1.4084            0.1425\n",
            "AR.3           -1.6840           -0.0000j            1.6840           -0.5000\n",
            "AR.4           -0.3155           -1.4607j            1.4944           -0.2839\n",
            "AR.5           -0.3155           +1.4607j            1.4944            0.2839\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aNeyzXdJaM5tuZoskLZX0pKSnJC01s0VmNk2Vk8LbG1QDImO4ByiuTCd8zewaSV+TNF/S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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QdDDQU27RzGanRm51XDdVWRV1ulbVB4AbgMtT0iLgOx2ct1LSfZKGJK1pc3yepOvS8Y2S\nlqb0cyTdlfs0JJ2Yjv04XbN57Dmd3arZgdFsqvIEQKuqTjvHzwdeAzwJEBH3A5P+wpbUA1wGvBlY\nDpwtaXlLtvcBj0fEccDngEvT9b8RESdGxInAO4EHI+Ku3HnnNI9HxO86vAezA6LRgJ6xtaq6XRqz\n4nUaOHZFxO7mTpoEONWPxApgKCK2pnPXsvds81XAVWn7BuCNktSS5+x0rtms0FxyxO/jsKrqNHD8\nRNLHgYMknQJ8C/j3Kc5ZBDyc2x9OaW3zRMQo8ASwoCXPO4BvtqR9NTVT/W2bQAOApPMkDUoa3L59\n+xRFNSvO+CVHHDisejoNHGuA7cA9wF8A64H/W1ahmiS9Cng6In6eSz4nIl4KvDZ93tnu3Ii4IiIG\nImJg4cKFZRfVbEwjP4+j0e3SmBWv01FVDUnfAb4TEZ3++b4NWJLbX5zS2uUZTs1fhwE7csdX01Lb\niIht6etTkq4laxLz0ic2Y3geh1XdpDUOZT4l6VHgPuC+9Pa/izq49ibgeEnLJPWTBYF1LXnWAeem\n7TOBHzaXNZFUA95Orn9DUq+ko9J2H9mLpX6O2QxSHzePo9ulMSveVE1Vf002muqkiDgyIo4EXgW8\nRtJfT3Zi6rO4ANgA3AtcHxGbJV0sqTnj/EpggaQh4EKyJrGmk4GHI2JrLm0esEHS3WQLLm4DvtzJ\njZodKBFBj6BWc+e4VdNUTVXvBE6JiEebCRGxNb1v/PtkQ2gnFBHryfpD8mkX5bZ3AmdNcO6PgVe3\npP0ReOUUZTbrqj3zOFzjsGqaqsbRlw8aTamfo6+cIpnNbo1G1lQl3Mdh1TRV4Ni9j8fM5qzxS444\ncFj1TNVU9TJJT7ZJFzC/hPKYzXqRfx9HtwtjVoJJA0dEeCFDs2mqj5vH4dBh1dPpBEAz61D+1bFu\nqbIqcuAwK1i+qcp9HFZFDhxmBRvrHK95OK5VkwOHWcH8Pg6rOgcOs4I153G4j8OqyoHDrGB75nG4\nxmHV5MBhVrBGBD01v4/DqsuBw6xgjbQ6rteqsqpy4DArWOSaqpr7ZlXiwGFWsEbu1bHNfbMqceAw\nK1ijpcbhfg6rGgcOs4LVGzHWxwEOHFY9DhxmBYtgbFRVc9+sShw4zArWbKqSm6qsohw4zAq2Z3Xc\nbN9xw6rGgcOsYM15HDX3cVhFlRo4JK2UdJ+kIUlr2hyfJ+m6dHyjpKUpfamkZyTdlT5fyp3zSkn3\npHO+oGYPpNkMEWNNVR6Oa9VUWuCQ1ANcBrwZWA6cLWl5S7b3AY9HxHHA54BLc8ceiIgT0+eDufR/\nAT4AHJ8+K8u6B7N9UW+0NlU5cli1lFnjWAEMRcTWiNgNrAVWteRZBVyVtm8A3jhZDULS0cChEXFr\nZD+NVwNnFF90s32XTQDEEwCtssoMHIuAh3P7wymtbZ6IGAWeABakY8sk3SnpJ5Jem8s/PMU1AZB0\nnqRBSYPbt2/fvzsx61CzdlGryRMArbJmauf4I8AxEfFy4ELgWkmHTucCEXFFRAxExMDChQtLKaRZ\nq2btouYJgFZhZQaObcCS3P7ilNY2j6Re4DBgR0TsiogdABFxO/AA8KKUf/EU1zTrmmaQyDdVOW5Y\n1ZQZODYBx0taJqkfWA2sa8mzDjg3bZ8J/DAiQtLC1LmOpGPJOsG3RsQjwJOSXp36Qt4F3FTiPZhN\nSzNwSG6qsurqLevCETEq6QJgA9ADfCUiNku6GBiMiHXAlcA1koaAx8iCC8DJwMWSRoAG8MGIeCwd\n+xDwNeAg4Ob0MZsRGo3sq1fHtSorLXAARMR6YH1L2kW57Z3AWW3O+zbw7QmuOQi8pNiSmhWjnmoX\nPbXckiOOHFYxM7Vz3GxWqjeafRxe5NCqy4HDrEDN2kVPTdTST5f7OKxqHDjMCrSnqcprVVl1OXCY\nFaiRa6ryWlVWVQ4cZgXK1ziaa+d4rSqrGgcOswI1O8d7PBzXKsyBw6xAY/M4cmtVBY4cVi0OHGYF\nGj+PI9U4Gt0skVnxHDjMCjR+HkeW5lFVVjUOHGYFarQZjuu4YVXjwGFWoHzneE+qcoy6rcoqxoHD\nrEBjTVU1Uat5AqBVkwOHWYHGmqokelPgqLvCYRXjwGFWoHpj7z4ON1VZ1ThwmBWokXvneG9Ps8bh\npiqrFgcOswI1m6XyneMOHFY1DhxmBdrTOZ4Fj3yaWVU4cJgVKN857hqHVZUDh1mB8p3j7uOwqnLg\nMCtQPdc53jM2qsqBw6ql1MAhaaWk+yQNSVrT5vg8Sdel4xslLU3pp0i6XdI96esbcuf8OF3zrvR5\nTpn3YDYdjTYzxz0B0Kqmt6wLS+oBLgNOAYaBTZLWRcSWXLb3AY9HxHGSVgOXAu8AHgXeFhG/lvQS\nYAOwKHfeORExWFbZzfbVuKaq9NLx0boDh1VLmTWOFcBQRGyNiN3AWmBVS55VwFVp+wbgjZIUEXdG\nxK9T+mbgIEnzSiyrWSHG5nFIpLjhPg6rnDIDxyLg4dz+MONrDePyRMQo8ASwoCXPnwF3RMSuXNpX\nUzPV36r50oMWks6TNChpcPv27ftzH2YdG5vHkatx1N1UZRUzozvHJZ1A1nz1F7nkcyLipcBr0+ed\n7c6NiCsiYiAiBhYuXFh+Yc0Y/yKnZo3DneNWNWUGjm3Aktz+4pTWNo+kXuAwYEfaXwzcCLwrIh5o\nnhAR29LXp4BryZrEzGaEkdGsytHXU9tT4/Aqh1YxZQaOTcDxkpZJ6gdWA+ta8qwDzk3bZwI/jIiQ\ndDjwH8CaiPh/zcySeiUdlbb7gLcCPy/xHsympbmgYW9Pbc8EQFc4rGJKCxypz+ICshFR9wLXR8Rm\nSRdLOj1luxJYIGkIuBBoDtm9ADgOuKhl2O08YIOku4G7yGosXy7rHsymayRFib5afua4axxWLaUN\nxwWIiPXA+pa0i3LbO4Gz2px3CXDJBJd9ZZFlNCvSaD3fVOX3cVg1zejOcbPZplnj6O1xjcOqy4HD\nrEAjjT01Di85YlXlwGFWoOYs8d70znHJEwCtehw4zAo0Um8gMdZM1ddTY7c7OaxiHDjMCjRSD/pq\nNZoLGszrqbF71IHDqsWBw6xAo/XG2Hs4APp7HTisehw4zAo02oixYbiQmqocOKxiHDjMCrS73qC/\nd8+PVX9vjRH3cVjFOHCYFWi03hhbowpSU5UDh1WMA4dZgUbrMb6Pw01VVkEOHGYF2tWmqWqXA4dV\njAOHWYGe2V3n4P6esX2PqrIqcuAwK9DTu0c5uG/P2qH9ngBoFeTAYVagZ3bXOailxuFRVVY1Dhxm\nBXq6panqoP4ent5d72KJzIrnwGFWoKdbahyHzu/lqZ2jXSyRWfEcOMwK9MzI+BrHIfP7eGrnSBdL\nZFY8Bw6zgkQEf9g5yrPm7ekcP2ReLztHGh5ZZZXiwGFWkCeeGWF3vcFzDpk/lnbI/CyIuNZhVeLA\nYVaQ3z65C4DnHjpvLO3IZ2fb2/+wqytlMitDqYFD0kpJ90kakrSmzfF5kq5LxzdKWpo79rGUfp+k\nUzu9plm3PLD9DwAsOvygsbRlC54FwEOP/rErZTIrQ+/UWfaNpB7gMuAUYBjYJGldRGzJZXsf8HhE\nHCdpNXAp8A5Jy4HVwAnA84EfSHpROmeqa5oVptEIdtcbjNQbPDNS5zdP7OT3T4/wvMPm86x5vQh4\ncucIP/zF7/j6T3/JofN7ecmiw8bOf+FznkV/b40b79zGSUuP5Mhn9QPphU89Gnvhk9lsUlrgAFYA\nQxGxFUDSWmAVkP8lvwr4VNq+Afiisp+kVcDaiNgFPChpKF2PDq5ZmE/ceA8bH3xsbD9i/Luj93qT\ndEy6u9f57fO0Ho/Jj0/xOuspy7wv33OK81tzTHV+lmfycu593/v3bwHQ2yPm9/bQ1yt6JEbqwa7R\nLEjsTl9Hp/G+8Fe+4AguPOVF9PXsqcgf3N/Lh173Qj7/g/vZsPm31ATNS/bWxCHzezm4v5daDUT2\njnIBtbShdO/1COqNoNGItA2NCEZGG/T0iIP7epifRnNFZMfqjRi33YjsudUju072DGoc3N/DvLS+\n1ri7jbab4559jKXl8+aO59PbPMp212rmDSJ93ZsgPass8DafWzMQN+NxM0/zOCmP0kXGndPm+1TB\nleeexDELDi70mmUGjkXAw7n9YeBVE+WJiFFJTwALUvqtLecuSttTXRMASecB5wEcc8wx+3QDzz/8\nIF783ENaLjzp7l5/Qe59fO/vM91r7F2GlvxTlrFdGaa4xhSFmO73nOr7tb/G5D/a0/keETDaaLBz\npM5IPfvF2t9bo6+nRn+P9mynr/N6s8/CQ+ZxxMH9/PapXTyze5QImNdXY8WyBeOaqPL+6k9fxOtf\n/Bxue/Axntw5giT6auLpkTpP7RzJJgimX/ABY78sG2mjVhM1QY9EraY9X2vZi6LqjeDp3XWeGamP\nBZ2a0tfaBNvpYYzUGzyzu54txNj8ZTvBMx+fzl7pE+VlXN5cnrbfb3xeqZmWz5UCSoztjT2zZsBJ\nB1JajAtw49Jy16iq/KKbRSkzcHRVRFwBXAEwMDCwT/8rzn/9cYWWyeauly05nJctObzbxTArRJmd\n49uAJbn9xSmtbR5JvcBhwI5Jzu3kmmZmVqIyA8cm4HhJyyT1k3V2r2vJsw44N22fCfwwskbPdcDq\nNOpqGXA8cFuH1zQzsxKV1lSV+iwuADYAPcBXImKzpIuBwYhYB1wJXJM6vx8jCwSkfNeTdXqPAudH\nRB2g3TXLugczM9ub2o30qZqBgYEYHBzsdjHMzGYVSbdHxEBrumeOm5nZtDhwmJnZtDhwmJnZtDhw\nmJnZtMyJznFJ24Ff7uPpRwGPFlicqvHzmZifzcT8bCY3U57PCyJiYWvinAgc+0PSYLtRBZbx85mY\nn83E/GwmN9Ofj5uqzMxsWhw4zMxsWhw4pnZFtwsww/n5TMzPZmJ+NpOb0c/HfRxmZjYtrnGYmdm0\nOHCYmdm0OHC0Iel/SwpJR6V9SfqCpCFJd0t6RS7vuZLuT59zJ77q7CbpHyX9It3/jZIOzx37WHo2\n90k6NZe+MqUNSVrTnZJ3x1y+dwBJSyT9SNIWSZslfTilHynplvTzcoukI1L6hD9jVSWpR9Kdkr6b\n9pdJ2piewXXp1RGk10tcl9I3SlrazXID6RWK/ox9yF4UtYFswuBRKe004Gay91e+GtiY0o8Etqav\nR6TtI7p9DyU9lzcBvWn7UuDStL0c+BkwD1gGPEC25H1P2j4W6E95lnf7Pg7Qs5qz9557BkcDr0jb\nhwD/nf6vfAZYk9LX5P4ftf0Zq/IHuBC4Fvhu2r8eWJ22vwT8Zdr+EPCltL0auK7bZXeNY2+fA/4P\njHsJ8Srg6sjcChwu6WjgVOCWiHgsIh4HbgFWHvASHwAR8f2IGE27t5K9fRGyZ7M2InZFxIPAELAi\nfYYiYmtE7AbWprxzwVy+dwAi4pGIuCNtPwXcCywiew5XpWxXAWek7Yl+xipJ0mLgLcC/pn0BbwBu\nSFlan03zmd0AvFH5F7x3gQNHjqRVwLaI+FnLoUXAw7n94ZQ2UXrVvZfsr0Pws2lnLt/7XlLTysuB\njcBzI+KRdOg3wHPT9lx7Zp8n+wO1kfYXAL/P/XGWv/+xZ5OOP5Hyd01pbwCcqST9AHhem0OfAD5O\n1iQzJ032bCLippTnE2RvZfzGgSybzU6Sng18G/iriHgy/4dyRISkOTcfQNJbgd9FxO2SXtft8uyL\nORc4IuJP26VLeilZG/3P0n/uxcAdklYA28j6PpoWp7RtwOta0n9ceKEPkImeTZOkdwNvBd4YqcGV\niZ8Nk6RX3WTPZM6Q1EcWNL4REf+Wkn8r6eiIeCQ1Rf0upc+lZ/Ya4HRJpwHzgUOBfyJrnutNtYr8\n/TefzbCkXuAwYMeBL3ZOtztZZuoHeIg9neNvYXzH3W0p/UjgQbKO8SPS9pHdLntJz2Ml2TvgF7ak\nn8D4zvGtZJ3DvWl7GXs6iE/o9n0coGc1Z+899wwEXA18viX9HxnfOf6ZtN32Z6zqH7I/PJud499i\nfOf4h9L2+YzvHL++2+WeczWOfbSebNTHEPA08B6AiHhM0t8Dm1K+iyPise4UsXRfJAsOt6Qa2a0R\n8cGI2CzperKgMgqcHxF1AEkXkI1Q6wG+EhGbu1P0AysiRufqvee8BngncI+ku1Lax4FPA9dLeh/Z\nyMW3p2Ntf8bmmI8CayVdAvpvaW8AAABISURBVNwJXJnSrwSukTQEPEYWPLrKS46Ymdm0eFSVmZlN\niwOHmZlNiwOHmZlNiwOHmZlNiwOHmZlNiwOHmZlNiwOHmZlNy/8HkFRIyJ3VGw0AAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                  0\n",
            "count  2.999000e+03\n",
            "mean   2.347572e-17\n",
            "std    1.113086e+01\n",
            "min   -1.872455e+02\n",
            "25%    1.179935e-17\n",
            "50%    1.179935e-17\n",
            "75%    1.179935e-17\n",
            "max    2.387206e+02\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_MPIBpMxuYx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pandas import datetime\n",
        "from matplotlib import pyplot\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2BJ4ZPRq83r0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# equipment = 3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQTdWczkRSZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "16c04249-a569-45bb-9076-22102279935e"
      },
      "source": [
        "len(list(train_x[0].reshape(-1)))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "128"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EO0zz7SCl63",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = list(train_x[0].reshape(-1))\n",
        "\n",
        "for i in range(train_x.shape[0] - 1):\n",
        "  history.append(train_x[i+1][-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXxgkjxkTDEM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "dd847e99-4c97-40fa-c565-6bc2df5fe66f"
      },
      "source": [
        "plt.plot(history)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5aa5454710>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k6XuBz+eTqSCpdC8iURCqO1SjxJehFiLUwGA6rUvEKLiLiBRRUIUsBXeJhHIY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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NicTLyx5Vtz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = list(train_x[0].reshape(-1))\n",
        "\n",
        "for i in range(1000):\n",
        "  history.append(train_x[-1000+i][-1])\n",
        "\n",
        "# history.append(x for x in test_x[0].reshape(-1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUojMt2FT8Tm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = ARIMA(history, order=(5,1,0))\n",
        "model_fit = model.fit(disp=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8dZiL_NWsFA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf328f63-354a-4299-fcdd-862ef60218de"
      },
      "source": [
        "history = list(test_x[0].reshape(-1))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<generator object <genexpr> at 0x7f5aa27d9bf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72cP-SxCBaB0",
        "colab_type": "code",
        "outputId": "0d0d5d89-dacb-4d4d-823a-66a2638ddca0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        }
      },
      "source": [
        "predictions = []\n",
        "# history = [x for x in test_x[i].reshape(-1) for i in range(1000)]\n",
        "for t in range(1000): \n",
        "    model = ARIMA(history, order=(5,1,0))\n",
        "    model_fit = model.fit(disp=0)\n",
        "    output = model_fit.forecast()\n",
        "    yhat = output[0]\n",
        "    predictions.append(yhat)\n",
        "    obs = test_y[t][0][0]\n",
        "    history.append(obs)\n",
        "    if(t%50==0):\n",
        "        print('predicted=%f, expected=%f' % (yhat, obs))\n",
        "\n",
        "predictions = np.array(predictions)\n",
        "print(predictions.shape)\n",
        "print(test_y.shape)\n",
        "error = mean_squared_error(test_y[:1000].reshape(-1), predictions)\n",
        "print('Test MSE: %.3f' % error)\n",
        "print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "print(\"MAE : %.3f\"%(mean_absolute_error(test_y[:1000].reshape(-1),predictions)))\n",
        "\n",
        "# plot\n",
        "pyplot.plot(test_y[:1000].reshape(-1))\n",
        "pyplot.plot(predictions)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000001, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=-0.000001, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=0.000001, expected=0.000000\n",
            "predicted=18.375338, expected=27.026667\n",
            "predicted=-0.000000, expected=0.000000\n",
            "predicted=-0.000000, expected=0.000000\n",
            "(1000, 1)\n",
            "(187509, 1, 1)\n",
            "Test MSE: 230.788\n",
            "RMSE : 15.192\n",
            "MAE : 2.277\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5aa27fe400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5fWTP_hEuOx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(arr = np.array(predictions), file = './drive/My Drive/arima_ukdale_preds_1000_eq'+eq+'.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCEnX4l79AA7",
        "colab_type": "code",
        "outputId": "10ba623c-3fc9-4325-be14-c9fb6bd461d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import time\n",
        "t1 = time.time()\n",
        "times = []\n",
        "for t in range(50): \n",
        "    model = ARIMA(history[t], order=(5,1,0))\n",
        "    model_fit = model.fit(disp=0)\n",
        "    t1 = time.time()\n",
        "    output = model_fit.forecast()\n",
        "    t2 = time.time()\n",
        "    times.append(t2-t1)\n",
        "print(times)\n",
        "print(sum(times))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.002229452133178711, 0.002145528793334961, 0.0022776126861572266, 0.0021119117736816406, 0.0020639896392822266, 0.0021719932556152344, 0.002135753631591797, 0.0022683143615722656, 0.0020749568939208984, 0.002238035202026367, 0.0021359920501708984, 0.002263784408569336, 0.0021483898162841797, 0.002351522445678711, 0.0020711421966552734, 0.0020694732666015625, 0.0021843910217285156, 0.0021543502807617188, 0.0020427703857421875, 0.0022361278533935547, 0.0021584033966064453, 0.002172708511352539, 0.0022726058959960938, 0.0019328594207763672, 0.0020842552185058594, 0.0019707679748535156, 0.0021820068359375, 0.0022058486938476562, 0.002017974853515625, 0.0020842552185058594, 0.0018773078918457031, 0.002106904983520508, 0.0020961761474609375, 0.0020842552185058594, 0.0021736621856689453, 0.001996278762817383, 0.0021402835845947266, 0.0023005008697509766, 0.002201080322265625, 0.002312183380126953, 0.0021886825561523438, 0.002158641815185547, 0.002132892608642578, 0.0021398067474365234, 0.002120494842529297, 0.002129793167114258, 0.002066373825073242, 0.0020627975463867188, 0.0019452571868896484, 0.002025127410888672]\n",
            "0.10671567916870117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWL4Ep0N8OhL",
        "colab_type": "code",
        "outputId": "f8c818d7-3c8d-4f96-c0d2-e66b985dfd70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "def mean_abs_pct_error(actual_values, forecast_values):\n",
        "\n",
        "    err=0\n",
        "    actual_values = pd.DataFrame(actual_values)\n",
        "    forecast_values = pd.DataFrame(forecast_values)\n",
        "    for i in range(len(forecast_values)):\n",
        "\n",
        "        err += np.abs(actual_values.values[i] - forecast_values.values[i])/actual_values.values[i]\n",
        "\n",
        "    return err[0] * 100/len(forecast_values) \n",
        "\n",
        "mean_abs_pct_error(test,predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-82697e50f568>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforecast_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mmean_abs_pct_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FRPSWNi8OhP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}