ForecastingImproved.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ForecastingImproved",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPpJtrgaF6l/WbAEZ1qbYtU"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "K546JY-OkPAM",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "outputId": "db327ebb-b05f-47e9-8972-ce86e6e293ee"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "!unzip df.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-de7ffbf5-fe21-4879-8057-b0cf4b43d541\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-de7ffbf5-fe21-4879-8057-b0cf4b43d541\">\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": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving df.zip to df (1).zip\n",
            "Archive:  df.zip\n",
            "replace df.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: df.csv                  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvuE2hoGkfmv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXk6UyTikhLL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "9dd38197-6d8f-452a-b66e-21344720407e"
      },
      "source": [
        "df = pd.read_csv('df.csv',parse_dates=True,index_col='date')\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            account_id  amount  balance\n",
            "date                                   \n",
            "1993-01-01        2378   700.0    700.0\n",
            "1993-01-01         576   900.0    900.0\n",
            "1993-01-01         704  1000.0   1000.0\n",
            "1993-01-01        3818   600.0    600.0\n",
            "1993-01-02        1972   400.0    400.0\n",
            "...                ...     ...      ...\n",
            "1998-12-31        2906    62.3  13729.4\n",
            "1998-12-31        2935    81.3  19544.9\n",
            "1998-12-31        2869    60.2  14638.2\n",
            "1998-12-31        2907   107.5  23453.0\n",
            "1998-12-31        2902   164.1  41642.9\n",
            "\n",
            "[1056320 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzDjJ2CIkkH5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "3917495d-ccc9-45c0-8742-3f6d1c37dcac"
      },
      "source": [
        "df_comp = df.groupby(df.index.strftime('%Y-%m')).sum()['amount']\n",
        "print(df_comp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "date\n",
            "1993-01    7.021576e+05\n",
            "1993-02    2.726925e+06\n",
            "1993-03    4.730319e+06\n",
            "1993-04    7.378368e+06\n",
            "1993-05    1.168075e+07\n",
            "               ...     \n",
            "1998-08    1.506393e+08\n",
            "1998-09    1.482977e+08\n",
            "1998-10    1.518896e+08\n",
            "1998-11    1.488421e+08\n",
            "1998-12    1.716971e+08\n",
            "Name: amount, Length: 72, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iM5Z1vDYk6pw",
        "colab_type": "text"
      },
      "source": [
        "<h2>Part for Regression starts from here<h/2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBSx9XAqkwnK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def NormalizeData(X):\n",
        "\tmean = np.mean(X,axis=0,keepdims=True)\n",
        "\tsigma = np.std(X,axis=0,keepdims=True)\n",
        "\tX_norm = (X-mean)/sigma;\n",
        "\treturn [X_norm,mean,sigma]\n",
        "\n",
        "def sgd_momentum(w, dw, learning_rate=None,config=None):\n",
        "\n",
        "    if config is None: config = {}\n",
        "    # config.setdefault('learning_rate', 1e-1)\n",
        "    config.setdefault('momentum', 0.9)\n",
        "    v = config.get('velocity', np.zeros_like(w))\n",
        "    if learning_rate==None:\n",
        "    \tlearning_rate = 1e-2\n",
        "\n",
        "\n",
        "    next_w = None\n",
        "    v  = config['momentum']*v - learning_rate*dw\n",
        "    next_w = w + v\n",
        "    config['velocity'] = v\n",
        "    return next_w, config\n",
        "\n",
        "def computeCost(X,y,theta,reg=None):\n",
        "\tif(reg==None):\n",
        "\t\treg = 0\n",
        "\n",
        "\tm = X.shape[0]\n",
        "\tJ = np.sum((X.dot(theta) - y)**2)/(2*m)\n",
        "\tJ += (reg*np.sum(theta**2))/(2*m)\n",
        "\tdtheta = ((X.T).dot(  X.dot(theta) - y  ))/m\n",
        "\tdtheta += reg*np.sum(theta)/m\n",
        "\treturn [J,dtheta];\n",
        "\n",
        "def gradientDescentWithMomentum(X,y,theta,num_iters,reg=None,learning_rate=None,printEpochs=None):\n",
        "\tm = X.shape[0]\n",
        "\tJ_history = np.zeros((num_iters, 1))\n",
        "\tdtheta = ((X.T).dot(  X.dot(theta) - y  ))/m\n",
        "\n",
        "\tfor i in range(num_iters):\n",
        "\t\ttheta,config = sgd_momentum(theta, dtheta,learning_rate, config=None)\n",
        "\t\t[J_history[i],dtheta] = computeCost(X,y,theta,reg);\n",
        "\t\tif(printEpochs==True):\n",
        "\t\t\tprint(\"Iteration \", i, \" : \", J_history[i])\n",
        "\t\tif(i>1 and abs(J_history[i]-J_history[i-1])<1e-5):\n",
        "\t\t\tbreak\n",
        "\n",
        "\treturn [theta,J_history]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXDGkyohlRWw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = np.arange(1,61).reshape(60,1)\n",
        "X = temp\n",
        "# X = np.hstack((temp,np.sqrt(temp)))\n",
        "# X = np.hstack((X,temp**2))\n",
        "# print(X.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1ZnDTWymQiA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.array(df_comp.iloc[0:60])\n",
        "# print(y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5d2_wialJOV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "97f95358-abf2-48a2-9e2f-006efaf1a3c7"
      },
      "source": [
        "num_training  = X.shape[0]\n",
        "try:\n",
        "  num_features  = X.shape[1]\n",
        "except:\n",
        "  num_features = 1;\n",
        "print(\"Number of training example :\", num_training)\n",
        "print(\"Number of Features :\", num_features)\n",
        "\n",
        "X = X.reshape((num_training,num_features))\n",
        "y = y.reshape((num_training,1))\n",
        "# [X,mean,sigma] = NormalizeData(X)\n",
        "\n",
        "X = np.hstack( (np.ones((num_training,1)),X ))\n",
        "theta = np.random.rand(num_features+1,1)\n",
        "print(\"Initial value of theta is : \\n\",theta)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training example : 60\n",
            "Number of Features : 1\n",
            "Initial value of theta is : \n",
            " [[0.9326166 ]\n",
            " [0.73800327]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hddDYmF0nmvG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7febbf2a-4233-4a15-e574-9286fb7e8845"
      },
      "source": [
        "num_iters= 2500\n",
        "reg = 0.5\n",
        "learning_rate = 1e-3\n",
        "[theta,J_history] = gradientDescentWithMomentum(X,y,theta,num_iters,reg,learning_rate,True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oThkIJk0opSo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "e6d565d6-f78b-4b01-ce66-ad300a7b9273"
      },
      "source": [
        "plt.plot(J_history)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbae6f390>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvzzTgGgovHG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "955223e6-069c-4adb-86d2-7beec1237997"
      },
      "source": [
        "pred = X.dot(theta)\n",
        "plt.plot(pred)\n",
        "plt.plot(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbaf41e80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ln1DpF5p20L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "15ffefab-7e25-46f7-a60a-8e1d07d6ff0b"
      },
      "source": [
        "print(theta)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-1724070.71451244]\n",
            " [ 2457287.0761572 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODO2nuacAoVx",
        "colab_type": "text"
      },
      "source": [
        "<h1>Regression Ends here </h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7UjYVYl1m3K",
        "colab_type": "text"
      },
      "source": [
        "<h2>Part 1 : Identifying Seasonality through Ratio </h2>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6p5P2yAjxzcZ",
        "colab_type": "text"
      },
      "source": [
        "Taking normal mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "109zyIjNp343",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "9a5a9654-c7e6-4697-8ea8-7a0ec79b2313"
      },
      "source": [
        "diff_mean = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    for j in range(k):\n",
        "      sum += df_comp[i + j*12]/pred[i + j*12]\n",
        "    diff_mean.append(sum/k)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_mean)\n",
        "print(len(diff_mean))\n",
        "# print(np.hstack((pred_l,temp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.95764039]), array([0.85470063]), array([0.83755206]), array([0.91033895]), array([1.10588431]), array([1.40494917]), array([1.16439658]), array([1.17255195]), array([1.14943416]), array([1.20666729]), array([1.12722844]), array([1.44840494]), array([1.18350749]), array([0.90598772]), array([0.95077478]), array([0.95382743]), array([1.07397006]), array([1.4044323]), array([1.0545111]), array([1.04562387]), array([1.04064341]), array([1.04867983]), array([0.99789205]), array([1.26483778]), array([1.15883518]), array([0.84595965]), array([0.91297493]), array([0.90841611]), array([1.00318805]), array([1.31415308]), array([0.97145747]), array([0.96542578]), array([0.95857222]), array([0.97040972]), array([0.93201703]), array([1.17720927]), array([1.13153386]), array([0.81905117]), array([0.88917811]), array([0.88628826]), array([0.96697919]), array([1.27805471]), array([0.94687856]), array([0.95032866]), array([0.95052583]), array([0.96552307]), array([0.93625419]), array([1.18624426]), array([1.15683551]), array([0.82887047]), array([0.90686281]), array([0.90484981]), array([0.97688483]), array([1.29813102]), array([0.95605751]), array([0.95979378]), array([0.96346381]), array([0.97687244]), array([0.94822106]), array([1.20063218])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWiaMRnox02b",
        "colab_type": "text"
      },
      "source": [
        "Taking weighted Mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Litp5xZGx9zc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "91fa3b09-9ff7-4d32-aa48-99563a2c4c1d"
      },
      "source": [
        "diff_mean_weighted = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    mul = 1\n",
        "    denom = 0;\n",
        "    for j in range(k):\n",
        "      denom += mul\n",
        "      sum += mul*df_comp[i + j*12]/pred[i + j*12]\n",
        "      mul -= 0.2\n",
        "\n",
        "    diff_mean_weighted.append(sum/denom)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_mean_weighted)\n",
        "print(len(diff_mean_weighted))\n",
        "# print(np.hstack((pred_l,temp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.95764039]), array([0.85470063]), array([0.83755206]), array([0.91033895]), array([1.10588431]), array([1.40494917]), array([1.16439658]), array([1.17255195]), array([1.14943416]), array([1.20666729]), array([1.12722844]), array([1.44840494]), array([1.15841115]), array([0.90028915]), array([0.93819448]), array([0.94899537]), array([1.07751609]), array([1.40448973]), array([1.0667206]), array([1.05972699]), array([1.05273127]), array([1.06623399]), array([1.01226276]), array([1.28523413]), array([1.146181]), array([0.85669274]), array([0.91298966]), array([0.9161449]), array([1.02354308]), array([1.33676596]), array([1.001378]), array([0.99605265]), array([0.98815591]), array([1.00314287]), array([0.95926381]), array([1.21441366]), array([1.13238798]), array([0.83978317]), array([0.89938938]), array([0.9023963]), array([0.99994444]), array([1.3129079]), array([0.98305855]), array([0.98305045]), array([0.97933174]), array([0.99567434]), array([0.95779265]), array([1.2142616]), array([1.14076493]), array([0.84167414]), array([0.90460353]), array([0.90750961]), array([1.00104863]), array([1.31727646]), array([0.9837062]), array([0.98402404]), array([0.981724]), array([0.99744737]), array([0.96034571]), array([1.21718975])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4hPN63X0EKe",
        "colab_type": "text"
      },
      "source": [
        "Weighted mean with starting values with higher weightage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mgblwcfs0LDR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "70391ed6-6cca-4a64-afc1-a7798259a6ba"
      },
      "source": [
        "diff_mean_weighted2 = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    mul = 0.2\n",
        "    denom = 0;\n",
        "    for j in range(k):\n",
        "      denom += mul\n",
        "      sum += mul*df_comp[i + j*12]/pred[i + j*12]\n",
        "      mul += 0.2\n",
        "\n",
        "    diff_mean_weighted2.append(sum/denom)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_mean_weighted2)\n",
        "print(len(diff_mean_weighted2))\n",
        "# print(np.hstack((pred_l,temp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.95764039]), array([0.85470063]), array([0.83755206]), array([0.91033895]), array([1.10588431]), array([1.40494917]), array([1.16439658]), array([1.17255195]), array([1.14943416]), array([1.20666729]), array([1.12722844]), array([1.44840494]), array([1.25879653]), array([0.92308341]), array([0.98851569]), array([0.96832359]), array([1.06333198]), array([1.40426]), array([1.01788261]), array([1.00331451]), array([1.00437983]), array([0.99601734]), array([0.95477992]), array([1.20364873]), array([1.18414354]), array([0.82449346]), array([0.91294545]), array([0.89295853]), array([0.962478]), array([1.26892733]), array([0.91161641]), array([0.90417206]), array([0.89940483]), array([0.90494342]), array([0.87752345]), array([1.10280048]), array([1.13033808]), array([0.79002638]), array([0.87488234]), array([0.863737]), array([0.92082785]), array([1.22926023]), array([0.89622658]), array([0.90451815]), array([0.91019757]), array([0.9233113]), array([0.90610034]), array([1.14701998]), array([1.17290609]), array([0.8160668]), array([0.90912209]), array([0.90219]), array([0.95272102]), array([1.27898557]), array([0.92840882]), array([0.93556352]), array([0.94520362]), array([0.9562975]), array([0.9360964]), array([1.18407462])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5EKKAhZqpwp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "28895c7d-4ba1-4c12-a310-ac320beedc54"
      },
      "source": [
        "temp = np.arange(1,73).reshape(72,1)\n",
        "X_new = temp\n",
        "# X_new = np.hstack((temp,np.sqrt(temp)))\n",
        "# X_new = np.hstack((X_new,temp**2))\n",
        "# [X_new,mean,sigma] = NormalizeData(X_new)\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] * (diff_mean[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  633394296.7564138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbb297358>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFckGoVVz3_A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "7c1983de-1edc-437c-9098-17a4f4f61957"
      },
      "source": [
        "temp = np.arange(1,73).reshape(72,1)\n",
        "X_new = temp\n",
        "# X_new = np.hstack((temp,np.sqrt(temp)))\n",
        "# X_new = np.hstack((X_new,temp**2))\n",
        "# [X_new,mean,sigma] = NormalizeData(X_new)\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] * (diff_mean_weighted[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  641075214.0485791\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbb098358>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDX0BAQf0hVi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "61cc6ab2-60e9-444c-e009-3dbc4f9b7a98"
      },
      "source": [
        "temp = np.arange(1,73).reshape(72,1)\n",
        "X_new = temp\n",
        "# X_new = np.hstack((temp,np.sqrt(temp)))\n",
        "# X_new = np.hstack((X_new,temp**2))\n",
        "# [X_new,mean,sigma] = NormalizeData(X_new)\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] * (diff_mean_weighted2[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  624155290.6226814\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbb6302e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwqsRn-irxH0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.plot(pred_new)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtXDhtia1zpV",
        "colab_type": "text"
      },
      "source": [
        "<h2>Part 2 : Identifying Seasonality through Difference </h2>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDSpuYoV15Ht",
        "colab_type": "text"
      },
      "source": [
        "Taking Difference from best fit line "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygwWsFiz13TQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "6f442682-b11a-4a34-e314-776036d88f1d"
      },
      "source": [
        "diff_2_mean = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    for j in range(k):\n",
        "      sum += (pred[i + j*12] - df_comp[i + j*12])\n",
        "    diff_2_mean.append(sum/k)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_2_mean)\n",
        "print(len(diff_2_mean))\n",
        "# print(np.hstack((pred_l,temp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([31058.76164476]), array([463578.13780196]), array([917471.91395917]), array([726709.79011637]), array([-1118388.7337264]), array([-5272297.15756946]), array([-2544355.88141239]), array([-3094585.70525531]), array([-3047188.62909819]), array([-4722099.65294106]), array([-3219653.976784]), array([-12449234.20062692]), array([-6170256.23141262]), array([929874.89474454]), array([-665547.62909833]), array([413805.84705883]), array([-1401358.02678398]), array([-11220784.50062695]), array([-27240.42446976]), array([380498.00168732]), array([175964.82784449]), array([499325.65400156]), array([1991322.73015872]), array([-8551022.04368417]), array([-6292661.99113649]), array([6299688.78502065]), array([3059384.59451108]), array([4354478.50400163]), array([2273189.74682545]), array([-10686551.84368043]), array([4812517.43247299]), array([5251998.64196344]), array([5555764.55145392]), array([5409511.92761106]), array([6938780.03710241]), array([-5757125.92007032]), array([-5826187.69252352]), array([10720563.38363171]), array([6581544.63479087]), array([7613693.03594802]), array([5211539.81210531]), array([-12321798.51173464]), array([6905774.56441989]), array([6464925.71557716]), array([6170097.04173446]), array([5424504.16789183]), array([6655608.36904972]), array([-10517010.67979045]), array([-10785992.49057732]), array([11770977.32557836]), array([5818913.98173733]), array([6617964.67789455]), array([3744952.1540521]), array([-19770155.08978847]), array([5717466.32636665]), array([5235690.08252394]), array([4515085.31868121]), array([3712489.17483874]), array([5436555.3909966]), array([-15937766.15284405])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwDPGFP314d_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "4ec00079-781d-4d8e-ffd7-1b6b5bdafd69"
      },
      "source": [
        "temp = np.arange(1,73).reshape(72,1)\n",
        "X_new = temp\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] - (diff_2_mean[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  620769310.9137936\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bb8cfbc88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iof6HcwK29J2",
        "colab_type": "text"
      },
      "source": [
        "Weighted Mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-hLRYOF2_1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "48c403cc-2ba5-4e85-aac6-86799962bb1c"
      },
      "source": [
        "diff_2_mean_weighted = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    mul = 1\n",
        "    denom = 0;\n",
        "    for j in range(k):\n",
        "      denom += mul\n",
        "      sum += mul*(pred[i + j*12] - df_comp[i + j*12])\n",
        "      mul -= 0.2\n",
        "\n",
        "    diff_2_mean_weighted.append(sum/denom)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_2_mean_weighted)\n",
        "print(len(diff_2_mean_weighted))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([31058.76164476]), array([463578.13780196]), array([917471.91395917]), array([726709.79011637]), array([-1118388.7337264]), array([-5272297.15756946]), array([-2544355.88141239]), array([-3094585.70525531]), array([-3047188.62909819]), array([-4722099.65294106]), array([-3219653.976784]), array([-12449234.20062692]), array([-5481221.23218402]), array([878064.14397314]), array([-489656.56875861]), array([448572.95184301]), array([-1369916.99422203]), array([-10559841.46250945]), array([-306919.9196856]), array([-5622.4101952]), array([-182163.33403803]), array([-80832.71343651]), array([1412325.31827619]), array([-8984156.72778892]), array([-5745284.30178408]), array([4918377.24937307]), array([2260069.83386352]), array([3395385.66835406]), array([1378133.57784456]), array([-10324402.72932893]), array([3392818.34682542]), array([3744533.17298252]), array([3942218.49913967]), array([3746846.58363013]), array([5267667.65145459]), array([-6780450.96405234]), array([-5556924.37248415]), array([7641921.52510047]), array([4386920.53697305]), array([5394807.23455879]), array([3185055.92500175]), array([-11310564.98455299]), array([4791779.43445901]), array([4652986.56775907]), array([4523772.21534488]), array([3992937.19864499]), array([5344585.61051703]), array([-9354195.82046642]), array([-7228143.52650471]), array([8197302.29631811]), array([4279018.59247639]), array([5210823.50196692]), array([2831292.29812425]), array([-13860766.07904971]), array([4536609.69710532]), array([4364037.2999292]), array([4081856.62941976]), array([3517703.33224375]), array([5025636.13506817]), array([-11238635.30210589])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTnQ9UKM3MaT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "81beba8b-d1cf-4889-8109-e094b9d2ce48"
      },
      "source": [
        "X_new = np.arange(1,73).reshape(72,1)\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] - (diff_2_mean_weighted[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  620951262.9943744\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bb9124208>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5lp5kK63a2c",
        "colab_type": "text"
      },
      "source": [
        "Weighted Mean with starting values with higher Weightage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJVV3lUS3f2N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "5433c928-6941-4202-e3a0-6a54135e5077"
      },
      "source": [
        "diff_2_mean_weighted2 = []\n",
        "sum = 0;\n",
        "# pred_l = pred_l.reshape((pred_l.shape[0],1))\n",
        "temp = np.array(df_comp[0:60]).reshape((60,1))\n",
        "\n",
        "for k in range(1,6):\n",
        "  for i in range(12):\n",
        "    mul = 0.2\n",
        "    denom = 0;\n",
        "    for j in range(k):\n",
        "      denom += mul\n",
        "      sum += mul*(pred[i + j*12]-df_comp[i + j*12])\n",
        "      mul += 0.2\n",
        "\n",
        "    diff_2_mean_weighted2.append(sum/denom)\n",
        "    sum = 0\n",
        "\n",
        "print(diff_2_mean_weighted2)\n",
        "print(len(diff_2_mean_weighted2))\n",
        "# print(np.hstack((pred_l,temp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([31058.76164476]), array([463578.13780196]), array([917471.91395917]), array([726709.79011637]), array([-1118388.7337264]), array([-5272297.15756946]), array([-2544355.88141239]), array([-3094585.70525531]), array([-3047188.62909819]), array([-4722099.65294106]), array([-3219653.976784]), array([-12449234.20062692]), array([-8237361.22909841]), array([1085307.14705873]), array([-1193220.8101175]), array([309504.53270632]), array([-1495681.12446984]), array([-13203613.61497944]), array([811798.06117779]), array([1538859.23733486]), array([1250349.31349205]), array([2239800.75631576]), array([3728314.96580629]), array([-7251617.99136992]), array([-7387417.36984133]), array([9062311.8563158]), array([4658014.1158062]), array([6272664.17529677]), array([4063302.08478723]), array([-11410850.07238342]), array([7651915.60376814]), array([8266929.57992527]), array([8782856.65608242]), array([8734842.61557291]), array([10281004.80839804]), array([-3710475.83210627]), array([-6203156.34057863]), array([15030661.98557543]), array([9654018.37173582]), array([10720133.15789294]), array([8048617.25405029]), array([-13737525.44978897]), array([9865367.74636511]), array([9001640.5225225]), array([8474951.79867988]), array([7428697.92483741]), array([8491040.23099548]), array([-12144951.4828441]), array([-14343841.45464994]), array([15344652.35483861]), array([7358809.37099827]), array([8025105.85382219]), array([4658612.00997995]), array([-25679544.10052725]), array([6898322.95562797]), array([6107342.86511869]), array([4948314.00794265]), array([3907275.01743373]), array([5847474.64692503]), array([-20636897.00358222])]\n",
            "60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmB_CC5B3ojM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "a91a4777-5f24-468a-89bf-442eda7eb404"
      },
      "source": [
        "X_new = np.arange(1,73).reshape(72,1)\n",
        "temp2 = np.hstack( (np.ones((72,1)),X_new ) )\n",
        "\n",
        "pred_new = temp2.dot(theta).reshape(72,1)\n",
        "# print(pred_new.shape)\n",
        "for i in range(60):\n",
        "  pred_new[12+i] = pred_new[12+i] - (diff_2_mean_weighted2[i])\n",
        "\n",
        "print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "plt.plot(np.arange(1,73),pred_new)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))\n",
        "# print(np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE :  620298312.2647784\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bb9403ba8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFDQG3Kz5xaI",
        "colab_type": "text"
      },
      "source": [
        "<h1>Part 3 : Seasonality Through ACF</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNJdC1My54A_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.acorr(df_comp,maxlags=48)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3g-e68K9Pt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "547b0059-60cf-493b-e123-e66296bdb092"
      },
      "source": [
        "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "ACF = plot_acf(df_comp,lags=60)\n",
        "PACf = plot_pacf(df_comp)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5h7zZc7QIXt5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def adf_check(time_series):\n",
        "    \"\"\"\n",
        "    Pass in a time series, returns ADF report\n",
        "    \"\"\"\n",
        "    result = adfuller(time_series)\n",
        "    print('Augmented Dickey-Fuller Test:')\n",
        "    labels = ['ADF Test Statistic','p-value','Number of Lags Used','Number of Observations Used']\n",
        "\n",
        "    for value,label in zip(result,labels):\n",
        "        print(label+' : '+str(value) )\n",
        "    \n",
        "    if result[1] <= 0.05:\n",
        "        print(\"strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\")\n",
        "    else:\n",
        "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYfmIcyfDGBR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "outputId": "9da821da-d3ce-4ef6-d50f-e7595ff3fc01"
      },
      "source": [
        "df_diff = df_comp.diff()\n",
        "df_diff_12 = df_diff.diff(12)\n",
        "df_diff_24 = df_diff_12.diff(12)\n",
        "df_diff_36 = df_diff_24.diff(12)\n",
        "\n",
        "\n",
        "# print(df_diff_12)\n",
        "# ACF = plot_acf(df_comp.dropna())\n",
        "# ACF = plot_acf(df_diff.dropna())\n",
        "# ACF = plot_acf(df_diff_12.dropna())\n",
        "# ACF = plot_acf(df_diff_24.dropna(),lags=24)\n",
        "# ACF = plot_acf(df_diff_36.dropna(),lags=24)\n",
        "\n",
        "# df_diff_24.dropna().plot()\n",
        "# df_diff_12.dropna().plot()\n",
        "# adf_check(df_comp.dropna())\n",
        "# adf_check(df_comp.diff().dropna())\n",
        "adf_check(df_comp.diff().diff().diff().dropna())\n",
        "# df_comp.diff().diff().dropna().plot()\n",
        "# print(df_comp.diff().diff().dropna().size)\n",
        "ACF = plot_acf(df_comp.diff().diff().diff(12).diff(12).dropna(),lags=45)\n",
        "PACf = plot_pacf(df_comp.diff().diff().diff(12).diff(12).dropna(),lags=45)\n",
        "\n",
        "\n",
        "# adf_check(df_comp.diff(12).dropna())\n",
        "# adf_check(df_comp.diff(12).diff().dropna())\n",
        "# adf_check(df_comp.diff(12).diff(12).dropna())\n",
        "# adf_check(df_comp.diff(12).diff(12).diff(12).diff().dropna())\n",
        "\n",
        "\n",
        "#Stationary one  : df_comp.diff(12).diff(12).diff(12).diff() , df_comp.diff().diff()\n",
        "\n",
        "\n",
        "# adf_check(df_diff.dropna())\n",
        "# adf_check(df_diff_12.dropna())\n",
        "# adf_check(df_diff_24.dropna())\n",
        "# adf_check(df_diff_36.dropna())\n",
        "# print(df_diff_36.dropna().size)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Augmented Dickey-Fuller Test:\n",
            "ADF Test Statistic : -4.26475064454048\n",
            "p-value : 0.000510965604918347\n",
            "Number of Lags Used : 11\n",
            "Number of Observations Used : 57\n",
            "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/regression/linear_model.py:1406: RuntimeWarning: invalid value encountered in sqrt\n",
            "  return rho, np.sqrt(sigmasq)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6SxiF97DvAi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "829cd525-714b-4693-9fe8-30d6a0d43e7e"
      },
      "source": [
        "df_log = np.log(df_comp)\n",
        "# df_log.plot()\n",
        "df_log.diff().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1bb9d04828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fScAd3HFETDy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "2a8f6633-7afa-461c-818a-7ea07cab8194"
      },
      "source": [
        "(df_log.diff() - df_log.diff().shift(12))[20:-1].plot()\n",
        "adf_check(df_log.dropna())\n",
        "adf_check(df_log.diff(12).dropna())\n",
        "# adf_check(df_log.diff(12).diff(12).dropna())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Augmented Dickey-Fuller Test:\n",
            "ADF Test Statistic : -0.9697363545213977\n",
            "p-value : 0.7641416500288573\n",
            "Number of Lags Used : 11\n",
            "Number of Observations Used : 60\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n",
            "\n",
            "Augmented Dickey-Fuller Test:\n",
            "ADF Test Statistic : 0.30132449053462024\n",
            "p-value : 0.9774051716736597\n",
            "Number of Lags Used : 4\n",
            "Number of Observations Used : 55\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59QqCp9Qfhb7",
        "colab_type": "text"
      },
      "source": [
        "AR Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-yxughWffAq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def AR(p,df):\n",
        "  df = df.dropna()\n",
        "  y = np.array(df.shift(p))\n",
        "  y = np.nan_to_num(y)\n",
        "  y = y.reshape(y.shape[0],1)\n",
        "  print(y)\n",
        "  n = df.shape[0]\n",
        "  num_variables = p\n",
        "  theta = np.random.rand(num_variables+ 1,1)\n",
        "  temp = np.array(df.shift(p))\n",
        "  temp = np.nan_to_num(temp).reshape(temp.shape[0],1)\n",
        "  zero = np.zeros((p,1))\n",
        "  temp = np.concatenate((zero, temp))\n",
        "  X = temp[0:p].reshape(1,p)\n",
        "  # print(X.shape)\n",
        "  for i in range(1,df.shape[0]):\n",
        "    X = np.vstack((X,temp[i:i+p].reshape(1,p)))\n",
        "  # print(X.shape)\n",
        "\n",
        "  # [X,mean,sigma] = NormalizeData(X)\n",
        "\n",
        "  X = np.hstack( (np.ones((y.shape[0],1)),X ))\n",
        "  # # print(X)\n",
        "  # print(y.shape,X.shape,theta.shape)\n",
        "\n",
        "  # print(y,X)\n",
        "\n",
        "  num_iters= 100000\n",
        "  reg = 0.5\n",
        "  learning_rate = 1e-16\n",
        "  [theta,J_history] = gradientDescentWithMomentum(X,y,theta,num_iters,reg,learning_rate,True);\n",
        "  # RMSE = \n",
        "  # print(\"RMSE : \", np.sqrt( (np.sum((pred_new-np.array(df_comp))**2))/72) )\n",
        "\n",
        "  return [X,theta,J_history]\n",
        "\n",
        "# theta = np.random.rand(num_features+1,1)\n",
        "# print(\"Initial value of theta is : \\n\",theta)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1BAn7b7uRb8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d0658cbb-450a-4b93-ffba-019c7fb2f081"
      },
      "source": [
        "p = 12\n",
        "[X_ar,theta,J_history] = AR(p,df_comp.diff().diff().dropna())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Iteration  95001  :  [5.53493205e+12]\n",
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            "Iteration  99953  :  [5.53044694e+12]\n",
            "Iteration  99954  :  [5.53044613e+12]\n",
            "Iteration  99955  :  [5.53044532e+12]\n",
            "Iteration  99956  :  [5.53044452e+12]\n",
            "Iteration  99957  :  [5.53044371e+12]\n",
            "Iteration  99958  :  [5.5304429e+12]\n",
            "Iteration  99959  :  [5.53044209e+12]\n",
            "Iteration  99960  :  [5.53044129e+12]\n",
            "Iteration  99961  :  [5.53044048e+12]\n",
            "Iteration  99962  :  [5.53043967e+12]\n",
            "Iteration  99963  :  [5.53043886e+12]\n",
            "Iteration  99964  :  [5.53043806e+12]\n",
            "Iteration  99965  :  [5.53043725e+12]\n",
            "Iteration  99966  :  [5.53043644e+12]\n",
            "Iteration  99967  :  [5.53043563e+12]\n",
            "Iteration  99968  :  [5.53043483e+12]\n",
            "Iteration  99969  :  [5.53043402e+12]\n",
            "Iteration  99970  :  [5.53043321e+12]\n",
            "Iteration  99971  :  [5.5304324e+12]\n",
            "Iteration  99972  :  [5.5304316e+12]\n",
            "Iteration  99973  :  [5.53043079e+12]\n",
            "Iteration  99974  :  [5.53042998e+12]\n",
            "Iteration  99975  :  [5.53042918e+12]\n",
            "Iteration  99976  :  [5.53042837e+12]\n",
            "Iteration  99977  :  [5.53042756e+12]\n",
            "Iteration  99978  :  [5.53042676e+12]\n",
            "Iteration  99979  :  [5.53042595e+12]\n",
            "Iteration  99980  :  [5.53042514e+12]\n",
            "Iteration  99981  :  [5.53042433e+12]\n",
            "Iteration  99982  :  [5.53042353e+12]\n",
            "Iteration  99983  :  [5.53042272e+12]\n",
            "Iteration  99984  :  [5.53042191e+12]\n",
            "Iteration  99985  :  [5.53042111e+12]\n",
            "Iteration  99986  :  [5.5304203e+12]\n",
            "Iteration  99987  :  [5.53041949e+12]\n",
            "Iteration  99988  :  [5.53041869e+12]\n",
            "Iteration  99989  :  [5.53041788e+12]\n",
            "Iteration  99990  :  [5.53041708e+12]\n",
            "Iteration  99991  :  [5.53041627e+12]\n",
            "Iteration  99992  :  [5.53041546e+12]\n",
            "Iteration  99993  :  [5.53041466e+12]\n",
            "Iteration  99994  :  [5.53041385e+12]\n",
            "Iteration  99995  :  [5.53041304e+12]\n",
            "Iteration  99996  :  [5.53041224e+12]\n",
            "Iteration  99997  :  [5.53041143e+12]\n",
            "Iteration  99998  :  [5.53041062e+12]\n",
            "Iteration  99999  :  [5.53040982e+12]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiNL_fFIorig",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "80d4ec91-fc85-4092-aee6-f89fccdf75c1"
      },
      "source": [
        "plt.plot(J_history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bbb04d080>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2sZDrfcqyn1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "bfbde0b3-8277-4db8-cae4-66835d964d03"
      },
      "source": [
        "print(theta)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.88188875]\n",
            " [-0.05922262]\n",
            " [-0.91346051]\n",
            " [-0.3608561 ]\n",
            " [-0.00546184]\n",
            " [ 0.09777539]\n",
            " [-0.03159593]\n",
            " [-0.39454906]\n",
            " [-0.83646048]\n",
            " [-1.26105532]\n",
            " [-1.49737653]\n",
            " [-1.55944687]\n",
            " [-1.37004847]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5oLCd6movGn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "b442977a-42e0-4067-99ed-a1e0991cb75e"
      },
      "source": [
        "# X = np.array(df[1:p+1]).reshape(1,p)\n",
        "pred = X_ar.dot(theta)\n",
        "print(pred.shape)\n",
        "plt.plot(np.arange(3,73),pred)\n",
        "plt.plot(np.arange(3,73),np.array(df_comp.diff().diff().dropna()))\n",
        "# print(pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bba8312b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZHbJu4XKvBq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = df_comp.diff().diff()\n",
        "temp += df_comp.shift(1)\n",
        "temp += df_comp.diff(1).shift(1)\n",
        "# temp.plot()\n",
        "# df_comp.diff().diff().shift(12).plot()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cL1fqzX1DC65",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "ec598bf1-0446-4b0e-c0fd-62ba56bfacb0"
      },
      "source": [
        "temp = np.zeros(df_comp.shape)\n",
        "temp += df_comp.shift(1)\n",
        "temp += df_comp.diff(1).shift(1)\n",
        "temp = temp.dropna()\n",
        "temp = np.array(temp)\n",
        "temp = temp.reshape(temp.shape[0],1)\n",
        "temp += pred\n",
        "\n",
        "print(pred.shape)\n",
        "plt.plot(np.arange(3,73),temp)\n",
        "plt.plot(np.arange(1,73),np.array(df_comp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1bba7b4438>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvlDIlQYsiDg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "6812485a-ad7b-4c9b-86db-d334cc076800"
      },
      "source": [
        "pip install statsmodels --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: statsmodels in /usr/local/lib/python3.6/dist-packages (0.11.1)\n",
            "Requirement already satisfied, skipping upgrade: patsy>=0.5 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (0.5.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.14 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=1.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.21 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (1.0.4)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5->statsmodels) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21->statsmodels) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21->statsmodels) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RR1gWVZ8RYOw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "d96fc170-a451-4209-ed4e-75ecfd27dee3"
      },
      "source": [
        "from statsmodels.tsa.ar_model import AutoReg\n",
        "model = AutoReg(df_comp.diff().diff().dropna(), lags=12)\n",
        "model_fit = model.fit()\n",
        "print('Coefficients: %s' % model_fit.params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: intercept    -593157.701726\n",
            "amount.L1         -1.322182\n",
            "amount.L2         -1.548130\n",
            "amount.L3         -1.555605\n",
            "amount.L4         -1.414333\n",
            "amount.L5         -1.014689\n",
            "amount.L6         -0.625043\n",
            "amount.L7         -0.230279\n",
            "amount.L8         -0.059155\n",
            "amount.L9         -0.052237\n",
            "amount.L10        -0.247538\n",
            "amount.L11        -0.706608\n",
            "amount.L12         0.044172\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQXjZKb4Sx-G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "c35aa9aa-1e44-492d-cb87-c5d6962d5316"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "model_fit.plot_predict(start=0, end=len(df_comp-2))\n",
        "# plt.plot(np.array(df_comp))\n",
        "plt.plot(np.arange(0,70),pred,color=\"red\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}