Time_Series_Forecasting_With_Prophet.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'> Time Series Forecasting With FBProphet </h1>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from prophet import Prophet \n",
    "from prophet.plot import plot_cross_validation_metric\n",
    "from prophet.diagnostics import performance_metrics, cross_validation\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_name</th>\n",
       "      <th>rest1</th>\n",
       "      <th>rest2</th>\n",
       "      <th>rest3</th>\n",
       "      <th>rest4</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>New Year's Day</td>\n",
       "      <td>65.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>24.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             weekday  holiday    holiday_name  rest1  rest2  rest3  rest4  \\\n",
       "date                                                                        \n",
       "2016-01-01    Friday        1  New Year's Day   65.0   25.0   67.0  139.0   \n",
       "2016-01-02  Saturday        0              na   24.0   39.0   43.0   85.0   \n",
       "2016-01-03    Sunday        0              na   24.0   31.0   66.0   81.0   \n",
       "2016-01-04    Monday        0              na   23.0   18.0   32.0   32.0   \n",
       "2016-01-05   Tuesday        0              na    2.0   15.0   38.0   43.0   \n",
       "\n",
       "            total  \n",
       "date               \n",
       "2016-01-01  296.0  \n",
       "2016-01-02  191.0  \n",
       "2016-01-03  202.0  \n",
       "2016-01-04  105.0  \n",
       "2016-01-05   98.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/RestaurantVisitors.csv\", infer_datetime_format=True, index_col='date', parse_dates=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2016-01-01 00:00:00'), Timestamp('2017-05-31 00:00:00'))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.min(), df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows where total is null\n",
    "df = df.dropna()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Forecasting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          ds      y\n",
       "0 2016-01-01  296.0\n",
       "1 2016-01-02  191.0\n",
       "2 2016-01-03  202.0\n",
       "3 2016-01-04  105.0\n",
       "4 2016-01-05   98.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing the data\n",
    "uni_data = df.reset_index()[['date', 'total']]\n",
    "uni_data = uni_data.rename(columns={'date': 'ds', 'total': 'y'})\n",
    "\n",
    "uni_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Valid Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape : (366, 2) and test shape : (112, 2)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data into training and validation sets\n",
    "split_date = '2017-01-01' \n",
    "train = uni_data[uni_data['ds'] < split_date]\n",
    "test = uni_data[uni_data['ds'] >= split_date]\n",
    "\n",
    "print(f\"train shape : {train.shape} and test shape : {test.shape}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:54:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "13:54:28 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x7fdd920a3e50>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "uni_model = Prophet()\n",
    "uni_model.fit(train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on the validation set\n",
    "future_uni = uni_model.make_future_dataframe(periods=len(test))\n",
    "forecast_uni = uni_model.predict(future_uni)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>144.465720</td>\n",
       "      <td>119.619746</td>\n",
       "      <td>199.574675</td>\n",
       "      <td>144.465720</td>\n",
       "      <td>144.465720</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>16.107317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>144.380252</td>\n",
       "      <td>196.197685</td>\n",
       "      <td>276.168883</td>\n",
       "      <td>144.380252</td>\n",
       "      <td>144.380252</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>94.079100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.459352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>144.294784</td>\n",
       "      <td>150.530955</td>\n",
       "      <td>230.531387</td>\n",
       "      <td>144.294784</td>\n",
       "      <td>144.294784</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>46.524846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.819631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>144.209317</td>\n",
       "      <td>60.840897</td>\n",
       "      <td>143.603463</td>\n",
       "      <td>144.209317</td>\n",
       "      <td>144.209317</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>-43.984644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.224673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>144.123849</td>\n",
       "      <td>60.359053</td>\n",
       "      <td>140.695715</td>\n",
       "      <td>144.123849</td>\n",
       "      <td>144.123849</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>-41.573185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.550664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2016-01-01  144.465720  119.619746  199.574675   144.465720   144.465720   \n",
       "1 2016-01-02  144.380252  196.197685  276.168883   144.380252   144.380252   \n",
       "2 2016-01-03  144.294784  150.530955  230.531387   144.294784   144.294784   \n",
       "3 2016-01-04  144.209317   60.840897  143.603463   144.209317   144.209317   \n",
       "4 2016-01-05  144.123849   60.359053  140.695715   144.123849   144.123849   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper     weekly  \\\n",
       "0       16.107317             16.107317             16.107317  16.107317   \n",
       "1       94.079100             94.079100             94.079100  94.079100   \n",
       "2       46.524846             46.524846             46.524846  46.524846   \n",
       "3      -43.984644            -43.984644            -43.984644 -43.984644   \n",
       "4      -41.573185            -41.573185            -41.573185 -41.573185   \n",
       "\n",
       "   weekly_lower  weekly_upper  multiplicative_terms  \\\n",
       "0     16.107317     16.107317                   0.0   \n",
       "1     94.079100     94.079100                   0.0   \n",
       "2     46.524846     46.524846                   0.0   \n",
       "3    -43.984644    -43.984644                   0.0   \n",
       "4    -41.573185    -41.573185                   0.0   \n",
       "\n",
       "   multiplicative_terms_lower  multiplicative_terms_upper        yhat  \n",
       "0                         0.0                         0.0  160.573037  \n",
       "1                         0.0                         0.0  238.459352  \n",
       "2                         0.0                         0.0  190.819631  \n",
       "3                         0.0                         0.0  100.224673  \n",
       "4                         0.0                         0.0  102.550664  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast_uni.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ds', 'y'], dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112, 3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_df_uni = forecast_uni[forecast_uni['ds'] >= split_date][['ds', 'yhat']]\n",
    "\n",
    "result = pd.merge(test, y_pred_df_uni, how='inner')\n",
    "\n",
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
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       "      <th>yhat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>256.0</td>\n",
       "      <td>187.714273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>130.0</td>\n",
       "      <td>97.339793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>144.0</td>\n",
       "      <td>99.886262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>99.0</td>\n",
       "      <td>102.316290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>109.854199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y        yhat\n",
       "0 2017-01-01  256.0  187.714273\n",
       "1 2017-01-02  130.0   97.339793\n",
       "2 2017-01-03  144.0   99.886262\n",
       "3 2017-01-04   99.0  102.316290\n",
       "4 2017-01-05   75.0  109.854199"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE from Univariate Analysis is : 35.36914997188645\n",
      "MAE from Univariate Analysis is : 28.912881360469708\n"
     ]
    }
   ],
   "source": [
    "#calculate rmse and mae\n",
    "rmse = np.sqrt(np.mean((result['y']-result['yhat'])**2))\n",
    "mae = (np.mean(np.abs(result['y']-result['yhat'])))\n",
    "\n",
    "print(f\"RMSE from Univariate Analysis is : {rmse}\")\n",
    "print(f\"MAE from Univariate Analysis is : {mae}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ds'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.set_index('ds').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "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>model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Univariate</td>\n",
       "      <td>35.36915</td>\n",
       "      <td>28.912881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        model      RMSE        MAE\n",
       "0  Univariate  35.36915  28.912881"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the metrics values\n",
    "perf_df = pd.DataFrame({\"model\": [\"Univariate\"], \"RMSE\": [rmse], \"MAE\": [mae]})\n",
    "perf_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Forecasting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "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>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday_Friday</th>\n",
       "      <th>weekday_Monday</th>\n",
       "      <th>weekday_Saturday</th>\n",
       "      <th>weekday_Sunday</th>\n",
       "      <th>weekday_Thursday</th>\n",
       "      <th>weekday_Tuesday</th>\n",
       "      <th>weekday_Wednesday</th>\n",
       "      <th>...</th>\n",
       "      <th>holiday_name_Memorial Day</th>\n",
       "      <th>holiday_name_Mother's Day</th>\n",
       "      <th>holiday_name_New Year's Day</th>\n",
       "      <th>holiday_name_New Year's Eve</th>\n",
       "      <th>holiday_name_Presidents Day</th>\n",
       "      <th>holiday_name_St. Patrick's Day</th>\n",
       "      <th>holiday_name_Thanksgiving</th>\n",
       "      <th>holiday_name_Valentine's Day</th>\n",
       "      <th>holiday_name_Veterans' Day</th>\n",
       "      <th>holiday_name_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>296.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y  holiday  weekday_Friday  weekday_Monday  \\\n",
       "0 2016-01-01  296.0        1               1               0   \n",
       "1 2016-01-02  191.0        0               0               0   \n",
       "2 2016-01-03  202.0        0               0               0   \n",
       "3 2016-01-04  105.0        0               0               1   \n",
       "4 2016-01-05   98.0        0               0               0   \n",
       "\n",
       "   weekday_Saturday  weekday_Sunday  weekday_Thursday  weekday_Tuesday  \\\n",
       "0                 0               0                 0                0   \n",
       "1                 1               0                 0                0   \n",
       "2                 0               1                 0                0   \n",
       "3                 0               0                 0                0   \n",
       "4                 0               0                 0                1   \n",
       "\n",
       "   weekday_Wednesday  ...  holiday_name_Memorial Day  \\\n",
       "0                  0  ...                          0   \n",
       "1                  0  ...                          0   \n",
       "2                  0  ...                          0   \n",
       "3                  0  ...                          0   \n",
       "4                  0  ...                          0   \n",
       "\n",
       "   holiday_name_Mother's Day  holiday_name_New Year's Day  \\\n",
       "0                          0                            1   \n",
       "1                          0                            0   \n",
       "2                          0                            0   \n",
       "3                          0                            0   \n",
       "4                          0                            0   \n",
       "\n",
       "   holiday_name_New Year's Eve  holiday_name_Presidents Day  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   holiday_name_St. Patrick's Day  holiday_name_Thanksgiving  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   holiday_name_Valentine's Day  holiday_name_Veterans' Day  holiday_name_na  \n",
       "0                             0                           0                0  \n",
       "1                             0                           0                1  \n",
       "2                             0                           0                1  \n",
       "3                             0                           0                1  \n",
       "4                             0                           0                1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing the data for multivariate time series analysis\n",
    "# Convert 'weekday' and 'holiday_name' to categorical variables using get dummies\n",
    "df = df.reset_index()\n",
    "df['weekday'] = pd.Categorical(df['weekday'])\n",
    "weekday_dummies = pd.get_dummies(df['weekday'], prefix='weekday')\n",
    "holiday_name_dummies = pd.get_dummies(df['holiday_name'], prefix='holiday_name')\n",
    "\n",
    "multi_data = pd.concat([df[['date', 'total', 'holiday']], weekday_dummies, holiday_name_dummies], axis=1)\n",
    "multi_data = multi_data.rename(columns={'date': 'ds', 'total': 'y'})\n",
    "\n",
    "multi_data.head()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training and validation sets\n",
    "split_date = '2017-01-01'  # replace with the date where you want to split the data\n",
    "train = multi_data[multi_data['ds'] < split_date]\n",
    "test = multi_data[multi_data['ds'] >= split_date]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ds', 'y', 'holiday', 'weekday_Friday', 'weekday_Monday',\n",
       "       'weekday_Saturday', 'weekday_Sunday', 'weekday_Thursday',\n",
       "       'weekday_Tuesday', 'weekday_Wednesday', 'holiday_name_Black Friday',\n",
       "       'holiday_name_Christmas Day', 'holiday_name_Christmas Eve',\n",
       "       'holiday_name_Cinco de Mayo', 'holiday_name_Columbus Day',\n",
       "       'holiday_name_Easter', 'holiday_name_Easter Monday',\n",
       "       'holiday_name_Father's Day', 'holiday_name_Good Friday',\n",
       "       'holiday_name_Groundhog Day', 'holiday_name_Halloween',\n",
       "       'holiday_name_Independence Day', 'holiday_name_Labor Day',\n",
       "       'holiday_name_Martin Luther King Day', 'holiday_name_Memorial Day',\n",
       "       'holiday_name_Mother's Day', 'holiday_name_New Year's Day',\n",
       "       'holiday_name_New Year's Eve', 'holiday_name_Presidents Day',\n",
       "       'holiday_name_St. Patrick's Day', 'holiday_name_Thanksgiving',\n",
       "       'holiday_name_Valentine's Day', 'holiday_name_Veterans' Day',\n",
       "       'holiday_name_na'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "holiday\n",
      "weekday_Friday\n",
      "weekday_Monday\n",
      "weekday_Saturday\n",
      "weekday_Sunday\n",
      "weekday_Thursday\n",
      "weekday_Tuesday\n",
      "weekday_Wednesday\n",
      "holiday_name_Black Friday\n",
      "holiday_name_Christmas Day\n",
      "holiday_name_Christmas Eve\n",
      "holiday_name_Cinco de Mayo\n",
      "holiday_name_Columbus Day\n",
      "holiday_name_Easter\n",
      "holiday_name_Easter Monday\n",
      "holiday_name_Father's Day\n",
      "holiday_name_Good Friday\n",
      "holiday_name_Groundhog Day\n",
      "holiday_name_Halloween\n",
      "holiday_name_Independence Day\n",
      "holiday_name_Labor Day\n",
      "holiday_name_Martin Luther King Day\n",
      "holiday_name_Memorial Day\n",
      "holiday_name_Mother's Day\n",
      "holiday_name_New Year's Day\n",
      "holiday_name_New Year's Eve\n",
      "holiday_name_Presidents Day\n",
      "holiday_name_St. Patrick's Day\n",
      "holiday_name_Thanksgiving\n",
      "holiday_name_Valentine's Day\n",
      "holiday_name_Veterans' Day\n",
      "holiday_name_na\n"
     ]
    }
   ],
   "source": [
    "# Initializing the Prophet model for multivariate analysis\n",
    "multi_model = Prophet()\n",
    "\n",
    "# Adding the additional regressors\n",
    "for column in multi_data.columns[2:]:\n",
    "    print(column)\n",
    "    multi_model.add_regressor(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13:54:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "13:54:29 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x7fdd9209bac0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "multi_model.fit(train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday_Friday</th>\n",
       "      <th>weekday_Monday</th>\n",
       "      <th>weekday_Saturday</th>\n",
       "      <th>weekday_Sunday</th>\n",
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       "      <th>weekday_Wednesday</th>\n",
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       "      <th>holiday_name_Mother's Day</th>\n",
       "      <th>holiday_name_New Year's Day</th>\n",
       "      <th>holiday_name_New Year's Eve</th>\n",
       "      <th>holiday_name_Presidents Day</th>\n",
       "      <th>holiday_name_St. Patrick's Day</th>\n",
       "      <th>holiday_name_Thanksgiving</th>\n",
       "      <th>holiday_name_Valentine's Day</th>\n",
       "      <th>holiday_name_Veterans' Day</th>\n",
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       "      <td>2016-01-04</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y  holiday  weekday_Friday  weekday_Monday  \\\n",
       "0 2016-01-01  296.0        1               1               0   \n",
       "1 2016-01-02  191.0        0               0               0   \n",
       "2 2016-01-03  202.0        0               0               0   \n",
       "3 2016-01-04  105.0        0               0               1   \n",
       "4 2016-01-05   98.0        0               0               0   \n",
       "\n",
       "   weekday_Saturday  weekday_Sunday  weekday_Thursday  weekday_Tuesday  \\\n",
       "0                 0               0                 0                0   \n",
       "1                 1               0                 0                0   \n",
       "2                 0               1                 0                0   \n",
       "3                 0               0                 0                0   \n",
       "4                 0               0                 0                1   \n",
       "\n",
       "   weekday_Wednesday  ...  holiday_name_Memorial Day  \\\n",
       "0                  0  ...                          0   \n",
       "1                  0  ...                          0   \n",
       "2                  0  ...                          0   \n",
       "3                  0  ...                          0   \n",
       "4                  0  ...                          0   \n",
       "\n",
       "   holiday_name_Mother's Day  holiday_name_New Year's Day  \\\n",
       "0                          0                            1   \n",
       "1                          0                            0   \n",
       "2                          0                            0   \n",
       "3                          0                            0   \n",
       "4                          0                            0   \n",
       "\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>221.404139</td>\n",
       "      <td>202.612344</td>\n",
       "      <td>268.749888</td>\n",
       "      <td>221.404139</td>\n",
       "      <td>221.404139</td>\n",
       "      <td>13.778163</td>\n",
       "      <td>13.778163</td>\n",
       "      <td>13.778163</td>\n",
       "      <td>-60.951786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.729949</td>\n",
       "      <td>74.729949</td>\n",
       "      <td>74.729949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>235.182302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>221.325142</td>\n",
       "      <td>151.709388</td>\n",
       "      <td>215.335697</td>\n",
       "      <td>221.325142</td>\n",
       "      <td>221.325142</td>\n",
       "      <td>-36.761310</td>\n",
       "      <td>-36.761310</td>\n",
       "      <td>-36.761310</td>\n",
       "      <td>-72.182768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.421458</td>\n",
       "      <td>35.421458</td>\n",
       "      <td>35.421458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.563832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>221.246145</td>\n",
       "      <td>56.062175</td>\n",
       "      <td>121.929762</td>\n",
       "      <td>221.246145</td>\n",
       "      <td>221.246145</td>\n",
       "      <td>-133.496314</td>\n",
       "      <td>-133.496314</td>\n",
       "      <td>-133.496314</td>\n",
       "      <td>-93.679432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-39.816881</td>\n",
       "      <td>-39.816881</td>\n",
       "      <td>-39.816881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.749831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>221.167147</td>\n",
       "      <td>68.944034</td>\n",
       "      <td>136.039425</td>\n",
       "      <td>221.167147</td>\n",
       "      <td>221.167147</td>\n",
       "      <td>-119.564879</td>\n",
       "      <td>-119.564879</td>\n",
       "      <td>-119.564879</td>\n",
       "      <td>-90.583549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-28.981330</td>\n",
       "      <td>-28.981330</td>\n",
       "      <td>-28.981330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.602269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2016-01-01  221.483136  264.347653  330.281888   221.483136   221.483136   \n",
       "1 2016-01-02  221.404139  202.612344  268.749888   221.404139   221.404139   \n",
       "2 2016-01-03  221.325142  151.709388  215.335697   221.325142   221.325142   \n",
       "3 2016-01-04  221.246145   56.062175  121.929762   221.246145   221.246145   \n",
       "4 2016-01-05  221.167147   68.944034  136.039425   221.167147   221.167147   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
       "0       76.098201             76.098201             76.098201   \n",
       "1       13.778163             13.778163             13.778163   \n",
       "2      -36.761310            -36.761310            -36.761310   \n",
       "3     -133.496314           -133.496314           -133.496314   \n",
       "4     -119.564879           -119.564879           -119.564879   \n",
       "\n",
       "   extra_regressors_additive  ...  weekday_Wednesday  weekday_Wednesday_lower  \\\n",
       "0                  66.436592  ...                0.0                      0.0   \n",
       "1                 -60.951786  ...                0.0                      0.0   \n",
       "2                 -72.182768  ...                0.0                      0.0   \n",
       "3                 -93.679432  ...                0.0                      0.0   \n",
       "4                 -90.583549  ...                0.0                      0.0   \n",
       "\n",
       "   weekday_Wednesday_upper     weekly  weekly_lower  weekly_upper  \\\n",
       "0                      0.0   9.661609      9.661609      9.661609   \n",
       "1                      0.0  74.729949     74.729949     74.729949   \n",
       "2                      0.0  35.421458     35.421458     35.421458   \n",
       "3                      0.0 -39.816881    -39.816881    -39.816881   \n",
       "4                      0.0 -28.981330    -28.981330    -28.981330   \n",
       "\n",
       "   multiplicative_terms  multiplicative_terms_lower  \\\n",
       "0                   0.0                         0.0   \n",
       "1                   0.0                         0.0   \n",
       "2                   0.0                         0.0   \n",
       "3                   0.0                         0.0   \n",
       "4                   0.0                         0.0   \n",
       "\n",
       "   multiplicative_terms_upper        yhat  \n",
       "0                         0.0  297.581337  \n",
       "1                         0.0  235.182302  \n",
       "2                         0.0  184.563832  \n",
       "3                         0.0   87.749831  \n",
       "4                         0.0  101.602269  \n",
       "\n",
       "[5 rows x 115 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictions on the validation set\n",
    "future_multi = multi_model.make_future_dataframe(periods=len(test))\n",
    "\n",
    "#add all the additional columns to future_multi\n",
    "future_multi = pd.merge(future_multi, multi_data.drop(\"y\", axis=1),how='left', on='ds')\n",
    "\n",
    "forecast_multi = multi_model.predict(future_multi)\n",
    "\n",
    "forecast_multi.head()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112, 2), (112, 34))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_df.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "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>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>256.0</td>\n",
       "      <td>326.710060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.098947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>144.0</td>\n",
       "      <td>98.133594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.516025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>75.0</td>\n",
       "      <td>104.993737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y        yhat\n",
       "0 2017-01-01  256.0  326.710060\n",
       "1 2017-01-02  130.0   84.098947\n",
       "2 2017-01-03  144.0   98.133594\n",
       "3 2017-01-04   99.0  100.516025\n",
       "4 2017-01-05   75.0  104.993737"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_df_multi = forecast_multi[forecast_multi['ds'] >= split_date][['ds', 'yhat']]\n",
    "\n",
    "result = pd.merge(test[['ds', 'y']], y_pred_df_multi, how='inner')\n",
    "\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE from Multivariate Analysis is : 29.828508962509343\n",
      "MAE from Multivariate Analysis is : 23.63975625167169\n"
     ]
    }
   ],
   "source": [
    "#calculate rmse\n",
    "rmse = np.sqrt(np.mean((result['y']-result['yhat'])**2))\n",
    "mae = (np.mean(np.abs(result['y']-result['yhat'])))\n",
    "\n",
    "print(f\"RMSE from Multivariate Analysis is : {rmse}\")\n",
    "print(f\"MAE from Multivariate Analysis is : {mae}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "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>model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Univariate</td>\n",
       "      <td>35.369150</td>\n",
       "      <td>28.912881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Multivariate</td>\n",
       "      <td>29.828509</td>\n",
       "      <td>23.639756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model       RMSE        MAE\n",
       "0    Univariate  35.369150  28.912881\n",
       "0  Multivariate  29.828509  23.639756"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_df = pd.concat([perf_df, pd.DataFrame({\"model\":[\"Multivariate\"], \"RMSE\":[rmse], \"MAE\":[mae]})])\n",
    "perf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ds'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.set_index('ds').dropna().plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It seems like multivariate analysis is performing better than the univariate analysis of visitors count!**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate vs Multivariate Modeling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='model'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf_df.set_index(\"model\").plot(kind='bar', figsize=(15, 6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On both count Root Mean Squared Error(RMSE) and Mean Absolute Error(MAE) Multivariate Prophet performing better than the univariate counterpart."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "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>Multivariate Prediction</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Univariate Prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>326.710060</td>\n",
       "      <td>256.0</td>\n",
       "      <td>187.714273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>84.098947</td>\n",
       "      <td>130.0</td>\n",
       "      <td>97.339793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>98.133594</td>\n",
       "      <td>144.0</td>\n",
       "      <td>99.886262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>100.516025</td>\n",
       "      <td>99.0</td>\n",
       "      <td>102.316290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>104.993737</td>\n",
       "      <td>75.0</td>\n",
       "      <td>109.854199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Multivariate Prediction  Actual  Univariate Prediction\n",
       "date                                                              \n",
       "2017-01-01               326.710060   256.0             187.714273\n",
       "2017-01-02                84.098947   130.0              97.339793\n",
       "2017-01-03                98.133594   144.0              99.886262\n",
       "2017-01-04               100.516025    99.0             102.316290\n",
       "2017-01-05               104.993737    75.0             109.854199"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = test[['ds', 'y']].rename({\"ds\":\"date\", \"y\":\"Actual\"}, axis=1)\n",
    "y_pred_df_multi = y_pred_df_multi.rename({\"ds\":\"date\", \"yhat\":\"Multivariate Prediction\"}, axis=1)\n",
    "y_pred_df_uni = y_pred_df_uni.rename({\"ds\":\"date\", \"yhat\":\"Univariate Prediction\"}, axis=1)\n",
    "\n",
    "plot_df = pd.merge(y_pred_df_multi, y, on=[\"date\"], how=\"inner\")\n",
    "plot_df = pd.merge(plot_df, y_pred_df_uni, on=[\"date\"], how=\"inner\").set_index(\"date\")\n",
    "\n",
    "plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot(figsize=(15, 7))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, Multivariate Analysis has upper edge in capturing the nuances of the variation in actual values over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}