Weather forecasting_Facebook Prophet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-Series Forecasting: Weather Forecasting Using Facebook’s Prophet Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an introductory tutorial on time-series forecasting using Facebook's prophet library.  \n",
    "\n",
    "**Disclaimer:** I will not be responsible for your financial loss if you blindly follow these. It is upto you to do your own research and invest carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # For all kinds of mathematical operations\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  # For plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://github.com/facebook/prophet\" style=\"color:blue;font-size:18px;\" ><strong>Facebook's prophet Library in Github </strong></a>\n",
    "\n",
    "**Installation**\n",
    "\n",
    "```\n",
    "pip install pystan\n",
    "\n",
    "pip install fbprophet\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fbprophet import Prophet  # library for forcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>996.57</td>\n",
       "      <td>-8.41</td>\n",
       "      <td>265.01</td>\n",
       "      <td>-9.28</td>\n",
       "      <td>93.4</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1309.80</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>136.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.51</td>\n",
       "      <td>264.91</td>\n",
       "      <td>-9.31</td>\n",
       "      <td>93.9</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1310.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.63</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.31</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>94.2</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1309.19</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>265.15</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>94.1</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1309.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>214.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
       "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
       "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
       "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
       "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
       "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
       "\n",
       "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
       "0          3.33          3.11          0.22       1.94             3.12   \n",
       "1          3.23          3.02          0.21       1.89             3.03   \n",
       "2          3.21          3.01          0.20       1.88             3.02   \n",
       "3          3.26          3.07          0.19       1.92             3.08   \n",
       "4          3.27          3.08          0.19       1.92             3.09   \n",
       "\n",
       "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0       1307.75      1.03           1.75     152.3  \n",
       "1       1309.80      0.72           1.50     136.1  \n",
       "2       1310.24      0.19           0.63     171.6  \n",
       "3       1309.19      0.34           0.50     198.0  \n",
       "4       1309.00      0.32           0.63     214.3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../data/jena_climate_2009_2016.csv')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print some statistics such as the mean, median, min, max and standard\n",
    "deviation values for the above features (columns). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "      <td>420551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>989.212776</td>\n",
       "      <td>9.450147</td>\n",
       "      <td>283.492743</td>\n",
       "      <td>4.955854</td>\n",
       "      <td>76.008259</td>\n",
       "      <td>13.576251</td>\n",
       "      <td>9.533756</td>\n",
       "      <td>4.042412</td>\n",
       "      <td>6.022408</td>\n",
       "      <td>9.640223</td>\n",
       "      <td>1216.062748</td>\n",
       "      <td>1.702224</td>\n",
       "      <td>3.056555</td>\n",
       "      <td>174.743738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.358481</td>\n",
       "      <td>8.423365</td>\n",
       "      <td>8.504471</td>\n",
       "      <td>6.730674</td>\n",
       "      <td>16.476175</td>\n",
       "      <td>7.739020</td>\n",
       "      <td>4.184164</td>\n",
       "      <td>4.896851</td>\n",
       "      <td>2.656139</td>\n",
       "      <td>4.235395</td>\n",
       "      <td>39.975208</td>\n",
       "      <td>65.446714</td>\n",
       "      <td>69.016932</td>\n",
       "      <td>86.681693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>913.600000</td>\n",
       "      <td>-23.010000</td>\n",
       "      <td>250.600000</td>\n",
       "      <td>-25.010000</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1059.450000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>984.200000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>277.430000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>65.210000</td>\n",
       "      <td>7.780000</td>\n",
       "      <td>6.210000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>1187.490000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.760000</td>\n",
       "      <td>124.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>989.580000</td>\n",
       "      <td>9.420000</td>\n",
       "      <td>283.470000</td>\n",
       "      <td>5.220000</td>\n",
       "      <td>79.300000</td>\n",
       "      <td>11.820000</td>\n",
       "      <td>8.860000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>5.590000</td>\n",
       "      <td>8.960000</td>\n",
       "      <td>1213.790000</td>\n",
       "      <td>1.760000</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>198.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>994.720000</td>\n",
       "      <td>15.470000</td>\n",
       "      <td>289.530000</td>\n",
       "      <td>10.070000</td>\n",
       "      <td>89.400000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>12.350000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>12.490000</td>\n",
       "      <td>1242.770000</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>4.740000</td>\n",
       "      <td>234.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1015.350000</td>\n",
       "      <td>37.280000</td>\n",
       "      <td>311.340000</td>\n",
       "      <td>23.110000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>63.770000</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>46.010000</td>\n",
       "      <td>18.130000</td>\n",
       "      <td>28.820000</td>\n",
       "      <td>1393.540000</td>\n",
       "      <td>28.490000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            p (mbar)       T (degC)       Tpot (K)    Tdew (degC)  \\\n",
       "count  420551.000000  420551.000000  420551.000000  420551.000000   \n",
       "mean      989.212776       9.450147     283.492743       4.955854   \n",
       "std         8.358481       8.423365       8.504471       6.730674   \n",
       "min       913.600000     -23.010000     250.600000     -25.010000   \n",
       "25%       984.200000       3.360000     277.430000       0.240000   \n",
       "50%       989.580000       9.420000     283.470000       5.220000   \n",
       "75%       994.720000      15.470000     289.530000      10.070000   \n",
       "max      1015.350000      37.280000     311.340000      23.110000   \n",
       "\n",
       "              rh (%)   VPmax (mbar)   VPact (mbar)   VPdef (mbar)  \\\n",
       "count  420551.000000  420551.000000  420551.000000  420551.000000   \n",
       "mean       76.008259      13.576251       9.533756       4.042412   \n",
       "std        16.476175       7.739020       4.184164       4.896851   \n",
       "min        12.950000       0.950000       0.790000       0.000000   \n",
       "25%        65.210000       7.780000       6.210000       0.870000   \n",
       "50%        79.300000      11.820000       8.860000       2.190000   \n",
       "75%        89.400000      17.600000      12.350000       5.300000   \n",
       "max       100.000000      63.770000      28.320000      46.010000   \n",
       "\n",
       "           sh (g/kg)  H2OC (mmol/mol)   rho (g/m**3)       wv (m/s)  \\\n",
       "count  420551.000000    420551.000000  420551.000000  420551.000000   \n",
       "mean        6.022408         9.640223    1216.062748       1.702224   \n",
       "std         2.656139         4.235395      39.975208      65.446714   \n",
       "min         0.500000         0.800000    1059.450000   -9999.000000   \n",
       "25%         3.920000         6.290000    1187.490000       0.990000   \n",
       "50%         5.590000         8.960000    1213.790000       1.760000   \n",
       "75%         7.800000        12.490000    1242.770000       2.860000   \n",
       "max        18.130000        28.820000    1393.540000      28.490000   \n",
       "\n",
       "       max. wv (m/s)       wd (deg)  \n",
       "count  420551.000000  420551.000000  \n",
       "mean        3.056555     174.743738  \n",
       "std        69.016932      86.681693  \n",
       "min     -9999.000000       0.000000  \n",
       "25%         1.760000     124.900000  \n",
       "50%         2.960000     198.100000  \n",
       "75%         4.740000     234.100000  \n",
       "max        23.500000     360.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the important features i.e. the date and price\n",
    "data_pred = data[[\"Date Time\",\"T (degC)\"]] # select Date and Price\n",
    "\n",
    "# Rename the features: These names are NEEDED for the model fitting\n",
    "data_pred = data_pred.rename(columns = {\"Date Time\":\"ds\",\"T (degC)\":\"y\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will not split the data into training and test sets but instead we will use all the data to fit the model and then ask the model to predict future values i.e. the stock price in 2021.\n",
    "\n",
    "\n",
    "Usually people split the data into training and testing because they do not want to train the model on the test set as well. If we keep a test set hidden, then the model will forecast values on unseen data. In that case, we would be also able to measure the error of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f3a8f9446d8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Prophet(daily_seasonality =True)\n",
    "model.fit(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "future = model.make_future_dataframe(periods=365) #we need to specify the number of days in future\n",
    "prediction = model.predict(future)\n",
    "model.plot(prediction)\n",
    "plt.title(\"Prediction weather using the Prophet\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Temperature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_components(prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other resources\n",
    "\n",
    "* [Weather Forecasting](https://github.com/ashBabu/Machine-Learning/blob/master/Weather%20Forecasting%20Time%20Series%20Forcasting%20LSTM%20(RNN).ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "* [Towards Data Science](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-facebooks-prophet-model-9ee1657132b5 \n",
    ")\n",
    "\n",
    "* https://github.com/facebook/prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venvTF2",
   "language": "python",
   "name": "venvtf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}