auto_arima_and_prophet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from fbprophet import Prophet\n",
    "\n",
    "from pyramid.arima import auto_arima\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "\n",
    "air_passengers_data = pd.read_csv(r\"D:\\time_series_session\\AirPassengers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the TIMESTAMP Column into DATETIME type\n",
    "\n",
    "air_passengers_data['Month'] = pd.to_datetime( air_passengers_data['Month'] )\n",
    "air_passengers_data.set_index( air_passengers_data['Month'], inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into TEST & TRAIN --- For time-series data you need to be careful with the SEQUENTIAL SPLIT\n",
    "\n",
    "air_passengers_data['Passengers'] = air_passengers_data['#Passengers']\n",
    "air_passengers_data.drop(columns=['#Passengers'], inplace=True)\n",
    "\n",
    "#divide into train and validation set\n",
    "train = air_passengers_data[:int(0.7*(len(air_passengers_data)))]\n",
    "valid = air_passengers_data[int(0.7*(len(air_passengers_data))):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13604c10>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove Redundant Columns\n",
    "#air_passengers_data.drop(columns=['Month'], inplace=True)\n",
    "\n",
    "#Plot the respective Train & Test data\n",
    "plt.plot(train.Passengers)\n",
    "plt.plot(valid.Passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(0, 0, 0, 1); AIC=886.147, BIC=901.718, Fit time=0.657 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 1); AIC=909.769, BIC=914.959, Fit time=0.015 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 1); AIC=907.370, BIC=915.155, Fit time=0.050 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 1); AIC=904.979, BIC=912.764, Fit time=0.085 seconds\n",
      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(0, 0, 0, 1); AIC=884.966, BIC=897.942, Fit time=0.425 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 1); AIC=900.136, BIC=910.517, Fit time=0.173 seconds\n",
      "Fit ARIMA: order=(1, 1, 3) seasonal_order=(0, 0, 0, 1); AIC=886.831, BIC=902.402, Fit time=0.645 seconds\n",
      "Fit ARIMA: order=(2, 1, 3) seasonal_order=(0, 0, 0, 1); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 2) seasonal_order=(0, 0, 0, 1); AIC=893.803, BIC=904.183, Fit time=0.358 seconds\n",
      "Total fit time: 2.425 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ARIMA(callback=None, disp=0, maxiter=50, method=None, order=(1, 1, 2),\n",
       "   out_of_sample_size=0, scoring='mse', scoring_args={},\n",
       "   seasonal_order=(0, 0, 0, 1), solver='lbfgs', start_params=None,\n",
       "   suppress_warnings=True, transparams=True, trend='c')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the AUTO-ARIMA Model\n",
    "\n",
    "auto_arima_model = auto_arima(train.Passengers, trace=True, error_action='ignore', suppress_warnings=True)\n",
    "auto_arima_model.fit(train.Passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecast Values for a Fixed Period\n",
    "\n",
    "forecast = auto_arima_model.predict(n_periods=len(valid))\n",
    "forecast = pd.DataFrame(forecast, index = valid.index, columns=['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x132e7f70>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the TRAIN, VALID & FORECAST values\n",
    "\n",
    "plt.plot(train.Passengers)\n",
    "plt.plot(valid.Passengers)\n",
    "plt.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.51299013857891\n"
     ]
    }
   ],
   "source": [
    "# Check for the RMSE Value\n",
    "\n",
    "rms = sqrt(mean_squared_error(valid.Passengers, forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Build a FORECAST Model using PROPHET\n",
    "\n",
    "air_passengers_data_prophet = pd.read_csv(r\"D:\\time_series_session\\AirPassengers.csv\")\n",
    "\n",
    "# Convert the Columns to PROPHET specific names\n",
    "simple_df = air_passengers_data_prophet.rename(columns={'Month': 'ds', '#Passengers': 'y'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'StanModel' object has no attribute 'fit_class'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-74-3a753eced2ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Fit the data to PROPHET Model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprophet_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mProphet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval_width\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.95\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprophet_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimple_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Produce Future Dates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\velmurugan.m\\envs\\velu\\lib\\site-packages\\fbprophet\\forecaster.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, df, **kwargs)\u001b[0m\n\u001b[0;32m   1095\u001b[0m             \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1097\u001b[1;33m                 \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1098\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;34m'algorithm'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\velmurugan.m\\envs\\velu\\lib\\site-packages\\pystan\\model.py\u001b[0m in \u001b[0;36moptimizing\u001b[1;34m(self, data, seed, init, sample_file, algorithm, verbose, as_vector, **kwargs)\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpystan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_seed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m         \u001b[0mfit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    512\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m         \u001b[0mm_pars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_param_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'StanModel' object has no attribute 'fit_class'"
     ]
    }
   ],
   "source": [
    "# Fit the data to PROPHET Model\n",
    "prophet_model = Prophet(interval_width=0.95)\n",
    "prophet_model.fit(simple_df)\n",
    "\n",
    "# Produce Future Dates\n",
    "#future_dates = prophet_model.make_future_dataframe(periods=18, freq='MS')\n",
    "#future_dates.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "air_passengers_data_prophet = pd.read_csv(r\"D:\\time_series_session\\AirPassengers.csv\")\n",
    "\n",
    "simple_df = air_passengers_data_prophet.rename(columns={'Month': 'ds', '#Passengers': 'y'})\n",
    "                                                        \n",
    "prophet_model = Prophet(interval_width=0.95)\n",
    "prophet_model.fit(simple_df)    \n",
    "\n",
    "future_dates = prophet_model.make_future_dataframe(periods=36, freq='MS')\n",
    "print (future_dates.tail())\n",
    "\n",
    "forecast = prophet_model.predict(future_dates)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\n",
    "\n",
    "prophet_model.plot(forecast, uncertainty=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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