AR-model-notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"AR_Model.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPCuk6xno24SDwUs2O53+j4"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"9BSwQx6J5wKj","colab_type":"code","outputId":"00c407cc-6d64-43f8-dd7b-d3d708ab5a6b","executionInfo":{"status":"ok","timestamp":1578838709073,"user_tz":-330,"elapsed":932,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":127,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"q1NmxSit57vd","colab_type":"code","colab":{}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import statsmodels.api as sm\n","from statsmodels.tsa.stattools import adfuller\n","from statsmodels.graphics.tsaplots import plot_pacf\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import mean_squared_error\n","from statsmodels.tsa.arima_model import ARIMA"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Kf83ssno6FOT","colab_type":"code","colab":{}},"source":["%matplotlib inline"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3Wa9VwPg6Hrs","colab_type":"code","colab":{}},"source":["file_path = \"/content/drive/My Drive/ts_example/ts_data.xlsx\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8AxwYELk6Q7c","colab_type":"code","colab":{}},"source":["df = pd.read_excel(file_path)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"eVJSYyfF6TM9","colab_type":"code","outputId":"b1f46bb5-f65f-4b2c-ab3f-309cc83e0a6f","executionInfo":{"status":"ok","timestamp":1578838771707,"user_tz":-330,"elapsed":873,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":203}},"source":["df.head()"],"execution_count":132,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>-0.090263</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>0.749995</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1.247887</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0.131282</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Time     Value\n","0     1  0.000000\n","1     2 -0.090263\n","2     3  0.749995\n","3     4  1.247887\n","4     5  0.131282"]},"metadata":{"tags":[]},"execution_count":132}]},{"cell_type":"code","metadata":{"id":"z-IOOMZpw6fO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"6076a107-d8f2-4c56-cff9-75eea815b3a1","executionInfo":{"status":"ok","timestamp":1578838789351,"user_tz":-330,"elapsed":857,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["df.shape"],"execution_count":133,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 2)"]},"metadata":{"tags":[]},"execution_count":133}]},{"cell_type":"code","metadata":{"id":"CTIYHu3R6VMy","colab_type":"code","outputId":"0169db54-74ab-4186-ff2e-77bc663bf424","executionInfo":{"status":"ok","timestamp":1578838810587,"user_tz":-330,"elapsed":1291,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["plt.plot(df[\"Value\"])"],"execution_count":134,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 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McFsXM23YSXYMDLPX7ULX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\nH1QDUB0WG+E/AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"fo3_AJIC6XVj","colab_type":"code","outputId":"0575d544-af85-4cbc-a790-10a70f5a2c00","executionInfo":{"status":"ok","timestamp":1578839025647,"user_tz":-330,"elapsed":1302,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":140}},"source":["X = df[\"Value\"].values\n","result = adfuller(X)\n","print('ADF Statistic: %f' % result[0])\n","print('p-value: %f' % result[1])\n","print('Critical Values:')\n","for key, value in result[4].items():\n","    print('\\t%s: %.3f' % (key, value))\n","\n","if result[0] < result[4][\"5%\"]:\n","    print (\"Reject Ho - Time Series is Stationary\")\n","else:\n","    print (\"Failed to Reject Ho - Time Series is Non-Stationary\")"],"execution_count":135,"outputs":[{"output_type":"stream","text":["ADF Statistic: -22.184930\n","p-value: 0.000000\n","Critical Values:\n","\t1%: -3.431\n","\t5%: -2.862\n","\t10%: -2.567\n","Reject Ho - Time Series is Stationary\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_zeam7XfFHOB","colab_type":"text"},"source":["<center><h1>Auto Regressive Model</h1></center>"]},{"cell_type":"markdown","metadata":{"id":"4UKgrhhdFDc4","colab_type":"text"},"source":["$$\n","Y_{t}=\\alpha+\\beta_{1} Y_{t-1}+\\beta_{2} Y_{t-2}+\\ldots+\\beta_{p} Y_{t-p}+\\epsilon_{1}\n","$$"]},{"cell_type":"code","metadata":{"id":"1pogyF9p6a1j","colab_type":"code","outputId":"8e31cd8c-3cbc-40fa-e218-9234c5f5d614","executionInfo":{"status":"ok","timestamp":1578839166087,"user_tz":-330,"elapsed":1156,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}},"colab":{"base_uri":"https://localhost:8080/","height":281}},"source":["plot_pacf(X, lags=10);"],"execution_count":136,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"EqhqcJYTxDGW","colab_type":"code","colab":{}},"source":["df[\"Values_shifted\"]  = df[\"Value\"].shift()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"j11XE1ALxTGR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"outputId":"be843f24-0a1f-4727-f9ec-d70702147747","executionInfo":{"status":"ok","timestamp":1578839394595,"user_tz":-330,"elapsed":808,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["df.head()"],"execution_count":138,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Time</th>\n","      <th>Value</th>\n","      <th>Values_shifted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>-0.090263</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>0.749995</td>\n","      <td>-0.090263</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1.247887</td>\n","      <td>0.749995</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0.131282</td>\n","      <td>1.247887</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Time     Value  Values_shifted\n","0     1  0.000000             NaN\n","1     2 -0.090263        0.000000\n","2     3  0.749995       -0.090263\n","3     4  1.247887        0.749995\n","4     5  0.131282        1.247887"]},"metadata":{"tags":[]},"execution_count":138}]},{"cell_type":"code","metadata":{"id":"OKgYGSIIxtKh","colab_type":"code","colab":{}},"source":["df.drop('Time', axis = 1, inplace=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ct5fJLnhx5HT","colab_type":"code","colab":{}},"source":["df.dropna(inplace=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4VtjnuDnyMTX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"outputId":"be97ce63-2908-45a5-f3fe-087649868020","executionInfo":{"status":"ok","timestamp":1578839462341,"user_tz":-330,"elapsed":855,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["df.head()"],"execution_count":141,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Value</th>\n","      <th>Values_shifted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.090263</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.749995</td>\n","      <td>-0.090263</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.247887</td>\n","      <td>0.749995</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.131282</td>\n","      <td>1.247887</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.562619</td>\n","      <td>0.131282</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Value  Values_shifted\n","1 -0.090263        0.000000\n","2  0.749995       -0.090263\n","3  1.247887        0.749995\n","4  0.131282        1.247887\n","5  0.562619        0.131282"]},"metadata":{"tags":[]},"execution_count":141}]},{"cell_type":"code","metadata":{"id":"mYyL8S_ZyNxu","colab_type":"code","colab":{}},"source":["y = df.Value.values\n","X = df.Values_shifted.values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Co7dza10ymZU","colab_type":"code","colab":{}},"source":["train_size = int(len(X) * 0.80)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ccfndRs0zAgI","colab_type":"code","colab":{}},"source":["X_train, X_test = X[0:train_size], X[train_size:len(X)]\n","y_train, y_test = y[0:train_size], y[train_size:len(X)]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jYowuwwbzaiM","colab_type":"code","colab":{}},"source":["X_train = X_train.reshape(-1,1)\n","X_test = X_test.reshape(-1,1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BDSas7QrzTub","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"3c9a6336-f0fe-4bf3-d2cc-f36ec4bd1e1a","executionInfo":{"status":"ok","timestamp":1578839673606,"user_tz":-330,"elapsed":961,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["lr = LinearRegression()\n","lr.fit(X_train, y_train)"],"execution_count":146,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"]},"metadata":{"tags":[]},"execution_count":146}]},{"cell_type":"code","metadata":{"id":"2GXGsDDuziSe","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"afe43077-18da-496e-f522-caf3d2f6c858","executionInfo":{"status":"ok","timestamp":1578839684590,"user_tz":-330,"elapsed":880,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["lr.coef_"],"execution_count":147,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.90550879])"]},"metadata":{"tags":[]},"execution_count":147}]},{"cell_type":"code","metadata":{"id":"eksDHekNzieX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"5ecc6437-ce3d-44a3-d66a-71f727033ad6","executionInfo":{"status":"ok","timestamp":1578839687274,"user_tz":-330,"elapsed":791,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["lr.intercept_"],"execution_count":148,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.06868111625452289"]},"metadata":{"tags":[]},"execution_count":148}]},{"cell_type":"code","metadata":{"id":"wgIb4in00WBv","colab_type":"code","colab":{}},"source":["y_pred = lr.predict(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"35PBumYqzwVJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":265},"outputId":"16d5975a-caef-4583-afb1-390ce0003931","executionInfo":{"status":"ok","timestamp":1578839745226,"user_tz":-330,"elapsed":1175,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["plt.plot(y_test[-10:], label=\"Actual Values\")\n","plt.plot(y_pred[-10:], label=\"Predicted Values\")\n","plt.legend()\n","plt.show()"],"execution_count":150,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHsV7vTLqfEoa08KPM3vbSa6kZ9Glvi/jOwfS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qdeKM6YQomQJ9vOkiperfXXP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\nrkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"KEKCjcnD0B28","colab_type":"code","colab":{}},"source":["model = ARIMA(y_train, order=(1,0,0))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tSfkEA6y0B6l","colab_type":"code","colab":{}},"source":["model_fit = model.fit()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0QZ77NIM0Bv4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":369},"outputId":"c873d158-8480-405a-8f26-39344338dbc1","executionInfo":{"status":"ok","timestamp":1578840176541,"user_tz":-330,"elapsed":954,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAGw0xp47aiUD_-aP4uJqXVZKRgT_wiBYTj7UJXbQ=s64","userId":"01561702845917398436"}}},"source":["print(model_fit.summary())"],"execution_count":153,"outputs":[{"output_type":"stream","text":["                              ARMA Model Results                              \n","==============================================================================\n","Dep. Variable:                      y   No. Observations:                 7999\n","Model:                     ARMA(1, 0)   Log Likelihood              -11364.832\n","Method:                       css-mle   S.D. of innovations              1.002\n","Date:                Sun, 12 Jan 2020   AIC                          22735.663\n","Time:                        14:42:57   BIC                          22756.624\n","Sample:                             0   HQIC                         22742.838\n","                                                                              \n","==============================================================================\n","                 coef    std err          z      P>|z|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","const          0.7250      0.118      6.130      0.000       0.493       0.957\n","ar.L1.y        0.9054      0.005    190.940      0.000       0.896       0.915\n","                                    Roots                                    \n","=============================================================================\n","                  Real          Imaginary           Modulus         Frequency\n","-----------------------------------------------------------------------------\n","AR.1            1.1045           +0.0000j            1.1045            0.0000\n","-----------------------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"H6Fuxt79NTGR","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}