notebooks/diagnostics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%matplotlib inline\n",
    "from prophet import Prophet\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import logging\n",
    "import warnings\n",
    "\n",
    "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:38:36 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:38:36 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35e541741f9c4809af8a301e3ae44e7b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:38:37 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:38:37 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:38:37 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:38:37 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:38:38 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:38:38 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "m = Prophet()\n",
    "m.fit(df)\n",
    "future = m.make_future_dataframe(periods=366)\n",
    "\n",
    "from prophet.diagnostics import cross_validation\n",
    "df_cv = cross_validation(\n",
    "    m, '365 days', initial='1825 days', period='365 days')\n",
    "cutoff = df_cv['cutoff'].unique()[0]\n",
    "df_cv = df_cv[df_cv['cutoff'].values == cutoff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(prophet)\n",
    "df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n",
    "m <- prophet(df)\n",
    "future <- make_future_dataframe(m, periods=366)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross validation\n",
    "\n",
    "Prophet includes functionality for time series cross validation to measure forecast error using historical data. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point. We can then compare the forecasted values to the actual values. This figure illustrates a simulated historical forecast on the Peyton Manning dataset, where the model was fit to an initial history of 5 years, and a forecast was made on a one year horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "input_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(facecolor='w', figsize=(10, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(m.history['ds'].values, m.history['y'], 'k.')\n",
    "ax.plot(df_cv['ds'].values, df_cv['yhat'], ls='-', c='#0072B2')\n",
    "ax.fill_between(df_cv['ds'].values, df_cv['yhat_lower'],\n",
    "                df_cv['yhat_upper'], color='#0072B2',\n",
    "                alpha=0.2)\n",
    "ax.axvline(x=pd.to_datetime(cutoff), c='gray', lw=4, alpha=0.5)\n",
    "ax.set_ylabel('y')\n",
    "ax.set_xlabel('ds')\n",
    "ax.text(x=pd.to_datetime('2010-01-01'),y=12, s='Initial', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8)\n",
    "ax.text(x=pd.to_datetime('2012-08-01'),y=12, s='Cutoff', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8)\n",
    "ax.axvline(x=pd.to_datetime(cutoff) + pd.Timedelta('365 days'), c='gray', lw=4,\n",
    "           alpha=0.5, ls='--')\n",
    "ax.text(x=pd.to_datetime('2013-01-01'),y=6, s='Horizon', color='black',\n",
    "       fontsize=16, fontweight='bold', alpha=0.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The Prophet paper](https://peerj.com/preprints/3190.pdf) gives further description of simulated historical forecasts.\n",
    "\n",
    "This cross validation procedure can be done automatically for a range of historical cutoffs using the `cross_validation` function. We specify the forecast horizon (`horizon`), and then optionally the size of the initial training period (`initial`) and the spacing between cutoff dates (`period`). By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon.\n",
    "\n",
    "The output of `cross_validation` is a dataframe with the true values `y` and the out-of-sample forecast values `yhat`, at each simulated forecast date and for each cutoff date. In particular, a forecast is made for every observed point between `cutoff` and `cutoff + horizon`. This dataframe can then be used to compute error measures of `yhat` vs. `y`.\n",
    "\n",
    "Here we do cross-validation to assess prediction performance on a horizon of 365 days, starting with 730 days of training data in the first cutoff and then making predictions every 180 days. On this 8 year time series, this corresponds to 11 total forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         y         ds     yhat yhat_lower yhat_upper     cutoff\n",
      "1 8.242493 2010-02-16 8.958128   8.498452   9.455592 2010-02-15\n",
      "2 8.008033 2010-02-17 8.724605   8.226165   9.226773 2010-02-15\n",
      "3 8.045268 2010-02-18 8.608332   8.079648   9.108332 2010-02-15\n",
      "4 7.928766 2010-02-19 8.530238   8.038721   9.024928 2010-02-15\n",
      "5 7.745003 2010-02-20 8.272207   7.761112   8.778963 2010-02-15\n",
      "6 7.866339 2010-02-21 8.603539   8.127878   9.087186 2010-02-15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Making 11 forecasts with cutoffs between 2010-02-15 and 2015-01-20\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\n",
    "head(df.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5adcf2eb7cd94e14a4ba3d84d414f595",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:39:04 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:04 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:04 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:04 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:04 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:05 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:05 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:05 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:05 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:05 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:06 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:06 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:06 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:06 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:06 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:07 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:07 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:07 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:08 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:08 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:08 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:09 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "from prophet.diagnostics import cross_validation\n",
    "df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>y</th>\n",
       "      <th>cutoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-16</td>\n",
       "      <td>8.954582</td>\n",
       "      <td>8.462876</td>\n",
       "      <td>9.452305</td>\n",
       "      <td>8.242493</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-17</td>\n",
       "      <td>8.720932</td>\n",
       "      <td>8.222682</td>\n",
       "      <td>9.242788</td>\n",
       "      <td>8.008033</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>8.604608</td>\n",
       "      <td>8.066920</td>\n",
       "      <td>9.144968</td>\n",
       "      <td>8.045268</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>8.526379</td>\n",
       "      <td>8.029189</td>\n",
       "      <td>9.043045</td>\n",
       "      <td>7.928766</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-20</td>\n",
       "      <td>8.268247</td>\n",
       "      <td>7.749520</td>\n",
       "      <td>8.741847</td>\n",
       "      <td>7.745003</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      yhat  yhat_lower  yhat_upper         y     cutoff\n",
       "0 2010-02-16  8.954582    8.462876    9.452305  8.242493 2010-02-15\n",
       "1 2010-02-17  8.720932    8.222682    9.242788  8.008033 2010-02-15\n",
       "2 2010-02-18  8.604608    8.066920    9.144968  8.045268 2010-02-15\n",
       "3 2010-02-19  8.526379    8.029189    9.043045  7.928766 2010-02-15\n",
       "4 2010-02-20  8.268247    7.749520    8.741847  7.745003 2010-02-15"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, the argument `units` must be a type accepted by `as.difftime`, which is weeks or shorter. In Python, the string for `initial`, `period`, and `horizon` should be in the format used by Pandas Timedelta, which accepts units of days or shorter.\n",
    "\n",
    "Custom cutoffs can also be supplied as a list of dates to the `cutoffs` keyword in the `cross_validation` function in Python and R. For example, three cutoffs six months apart, would need to be passed to the `cutoffs` argument in a date format like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "cutoffs <- as.Date(c('2013-02-15', '2013-08-15', '2014-02-15'))\n",
    "df.cv2 <- cross_validation(m, cutoffs = cutoffs, horizon = 365, units = 'days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49bd5a3e78c04c848a47966f18a69fbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:39:15 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:15 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:15 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:15 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:16 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:16 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "cutoffs = pd.to_datetime(['2013-02-15', '2013-08-15', '2014-02-15'])\n",
    "df_cv2 = cross_validation(m, cutoffs=cutoffs, horizon='365 days')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `performance_metrics` utility can be used to compute some useful statistics of the prediction performance (`yhat`, `yhat_lower`, and `yhat_upper` compared to `y`), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), median absolute percent error (MDAPE) and coverage of the `yhat_lower` and `yhat_upper` estimates. These are computed on a rolling window of the predictions in `df_cv` after sorting by horizon (`ds` minus `cutoff`). By default 10% of the predictions will be included in each window, but this can be changed with the `rolling_window` argument.\n",
    "\n",
    "In Python, you can also create custom performance metric using the `register_performance_metric` decorator. Created metric should contain following arguments:\n",
    " - df: Cross-validation results dataframe.\n",
    " - w: Aggregation window size.\n",
    " \n",
    "and return:\n",
    "- Dataframe with columns horizon and metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  horizon       mse      rmse       mae       mape      mdape      smape\n",
      "1 37 days 0.4939037 0.7027828 0.5048970 0.05850212 0.04980376 0.05879513\n",
      "2 38 days 0.4997148 0.7069051 0.5098831 0.05907908 0.05004985 0.05943198\n",
      "3 39 days 0.5218764 0.7224101 0.5159664 0.05967708 0.04980376 0.06015530\n",
      "4 40 days 0.5290861 0.7273830 0.5188235 0.05998232 0.04980376 0.06052964\n",
      "5 41 days 0.5364765 0.7324455 0.5198071 0.06006024 0.04934500 0.06067036\n",
      "6 42 days 0.5402054 0.7349866 0.5201720 0.06007814 0.04980376 0.06074102\n",
      "   coverage\n",
      "1 0.6765646\n",
      "2 0.6754226\n",
      "3 0.6726816\n",
      "4 0.6713111\n",
      "5 0.6788488\n",
      "6 0.6841023\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "df.p <- performance_metrics(df.cv)\n",
    "head(df.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>horizon</th>\n",
       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "      <th>mdape</th>\n",
       "      <th>smape</th>\n",
       "      <th>coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37 days</td>\n",
       "      <td>0.493358</td>\n",
       "      <td>0.702395</td>\n",
       "      <td>0.503977</td>\n",
       "      <td>0.058376</td>\n",
       "      <td>0.049365</td>\n",
       "      <td>0.058677</td>\n",
       "      <td>0.676565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38 days</td>\n",
       "      <td>0.499112</td>\n",
       "      <td>0.706478</td>\n",
       "      <td>0.508946</td>\n",
       "      <td>0.058951</td>\n",
       "      <td>0.049135</td>\n",
       "      <td>0.059312</td>\n",
       "      <td>0.675423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39 days</td>\n",
       "      <td>0.521344</td>\n",
       "      <td>0.722042</td>\n",
       "      <td>0.515016</td>\n",
       "      <td>0.059547</td>\n",
       "      <td>0.049225</td>\n",
       "      <td>0.060034</td>\n",
       "      <td>0.672682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40 days</td>\n",
       "      <td>0.528651</td>\n",
       "      <td>0.727084</td>\n",
       "      <td>0.517873</td>\n",
       "      <td>0.059852</td>\n",
       "      <td>0.049072</td>\n",
       "      <td>0.060409</td>\n",
       "      <td>0.676336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41 days</td>\n",
       "      <td>0.536149</td>\n",
       "      <td>0.732222</td>\n",
       "      <td>0.518843</td>\n",
       "      <td>0.059927</td>\n",
       "      <td>0.049135</td>\n",
       "      <td>0.060548</td>\n",
       "      <td>0.681361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  horizon       mse      rmse       mae      mape     mdape     smape   \n",
       "0 37 days  0.493358  0.702395  0.503977  0.058376  0.049365  0.058677  \\\n",
       "1 38 days  0.499112  0.706478  0.508946  0.058951  0.049135  0.059312   \n",
       "2 39 days  0.521344  0.722042  0.515016  0.059547  0.049225  0.060034   \n",
       "3 40 days  0.528651  0.727084  0.517873  0.059852  0.049072  0.060409   \n",
       "4 41 days  0.536149  0.732222  0.518843  0.059927  0.049135  0.060548   \n",
       "\n",
       "   coverage  \n",
       "0  0.676565  \n",
       "1  0.675423  \n",
       "2  0.672682  \n",
       "3  0.676336  \n",
       "4  0.681361  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from prophet.diagnostics import performance_metrics\n",
    "df_p = performance_metrics(df_cv)\n",
    "df_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>horizon</th>\n",
       "      <th>mase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37 days</td>\n",
       "      <td>0.522946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38 days</td>\n",
       "      <td>0.528102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39 days</td>\n",
       "      <td>0.534401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40 days</td>\n",
       "      <td>0.537365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41 days</td>\n",
       "      <td>0.538372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  horizon      mase\n",
       "0 37 days  0.522946\n",
       "1 38 days  0.528102\n",
       "2 39 days  0.534401\n",
       "3 40 days  0.537365\n",
       "4 41 days  0.538372"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from prophet.diagnostics import register_performance_metric, rolling_mean_by_h\n",
    "import numpy as np\n",
    "@register_performance_metric\n",
    "def mase(df, w):\n",
    "    \"\"\"Mean absolute scale error\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        df: Cross-validation results dataframe.\n",
    "        w: Aggregation window size.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        Dataframe with columns horizon and mase.\n",
    "    \"\"\"\n",
    "    e = (df['y'] - df['yhat'])\n",
    "    d = np.abs(np.diff(df['y'])).sum()/(df['y'].shape[0]-1)\n",
    "    se = np.abs(e/d)\n",
    "    if w < 0:\n",
    "        return pd.DataFrame({'horizon': df['horizon'], 'mase': se})\n",
    "    return rolling_mean_by_h(\n",
    "        x=se.values, h=df['horizon'].values, w=w, name='mase'\n",
    "    )\n",
    "\n",
    "df_mase = performance_metrics(df_cv, metrics=['mase'])\n",
    "df_mase.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation performance metrics can be visualized with `plot_cross_validation_metric`, here shown for MAPE. Dots show the absolute percent error for each prediction in `df_cv`. The blue line shows the MAPE, where the mean is taken over a rolling window of the dots. We see for this forecast that errors around 5% are typical for predictions one month into the future, and that errors increase up to around 11% for predictions that are a year out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 10 -h 6 -u in\n",
    "plot_cross_validation_metric(df.cv, metric = 'mape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from prophet.plot import plot_cross_validation_metric\n",
    "fig = plot_cross_validation_metric(df_cv, metric='mape')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallelizing cross validation\n",
    "\n",
    "Cross-validation can also be run in parallel mode in Python, by setting specifying the `parallel` keyword. Four modes are supported\n",
    "\n",
    "* `parallel=None` (Default, no parallelization)\n",
    "* `parallel=\"processes\"`\n",
    "* `parallel=\"threads\"`\n",
    "* `parallel=\"dask\"`\n",
    "\n",
    "For problems that aren't too big, we recommend using `parallel=\"processes\"`. It will achieve the highest performance when the parallel cross validation can be done on a single machine. For large problems, a [Dask](https://dask.org) cluster can be used to do the cross validation on many machines. You will need to [install Dask](https://docs.dask.org/en/latest/install.html) separately, as it will not be installed with `prophet`.\n",
    "\n",
    "\n",
    "```python\n",
    "from dask.distributed import Client\n",
    "\n",
    "client = Client()  # connect to the cluster\n",
    "df_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days',\n",
    "                         parallel=\"dask\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n",
    "\n",
    "Cross-validation can be used for tuning hyperparameters of the model, such as `changepoint_prior_scale` and `seasonality_prior_scale`. A Python example is given below, with a 4x4 grid of those two parameters, with parallelization over cutoffs. Here parameters are evaluated on RMSE averaged over a 30-day horizon, but different performance metrics may be appropriate for different problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:39:23 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:23 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:24 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:25 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:26 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:28 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:29 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:30 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:31 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:31 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:31 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:32 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:33 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:33 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:34 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:35 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:35 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:36 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:37 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:37 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:38 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:39 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:40 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:41 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:42 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:42 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:42 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:42 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:43 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:43 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:43 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:43 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:44 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:44 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:44 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:44 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:39:45 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:45 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:45 - cmdstanpy - INFO - Chain [1] done processing\n",
      "19:39:45 - cmdstanpy - INFO - Chain [1] start processing\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "param_grid = {  \n",
    "    'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\n",
    "    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],\n",
    "}\n",
    "\n",
    "# Generate all combinations of parameters\n",
    "all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\n",
    "rmses = []  # Store the RMSEs for each params here\n",
    "\n",
    "# Use cross validation to evaluate all parameters\n",
    "for params in all_params:\n",
    "    m = Prophet(**params).fit(df)  # Fit model with given params\n",
    "    df_cv = cross_validation(m, cutoffs=cutoffs, horizon='30 days', parallel=\"processes\")\n",
    "    df_p = performance_metrics(df_cv, rolling_window=1)\n",
    "    rmses.append(df_p['rmse'].values[0])\n",
    "\n",
    "# Find the best parameters\n",
    "tuning_results = pd.DataFrame(all_params)\n",
    "tuning_results['rmse'] = rmses\n",
    "print(tuning_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = all_params[np.argmin(rmses)]\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, parallelization could be done across parameter combinations by parallelizing the loop above.\n",
    "\n",
    "The Prophet model has a number of input parameters that one might consider tuning. Here are some general recommendations for hyperparameter tuning that may be a good starting place.\n",
    "\n",
    "**Parameters that can be tuned**\n",
    "- `changepoint_prior_scale`: This is probably the most impactful parameter. It determines the flexibility of the trend, and in particular how much the trend changes at the trend changepoints. As described in this documentation, if it is too small, the trend will be underfit and variance that should have been modeled with trend changes will instead end up being handled with the noise term. If it is too large, the trend will overfit and in the most extreme case you can end up with the trend capturing yearly seasonality. The default of 0.05 works for many time series, but this could be tuned; a range of [0.001, 0.5] would likely be about right. Parameters like this (regularization penalties; this is effectively a lasso penalty) are often tuned on a log scale.\n",
    "\n",
    "- `seasonality_prior_scale`: This parameter controls the flexibility of the seasonality. Similarly, a large value allows the seasonality to fit large fluctuations, a small value shrinks the magnitude of the seasonality. The default is 10., which applies basically no regularization. That is because we very rarely see overfitting here (there's inherent regularization with the fact that it is being modeled with a truncated Fourier series, so it's essentially low-pass filtered). A reasonable range for tuning it would probably be [0.01, 10]; when set to 0.01 you should find that the magnitude of seasonality is forced to be very small. This likely also makes sense on a log scale, since it is effectively an L2 penalty like in ridge regression.\n",
    "\n",
    "- `holidays_prior_scale`: This controls flexibility to fit holiday effects. Similar to seasonality_prior_scale, it defaults to 10.0 which applies basically no regularization, since we usually have multiple observations of holidays and can do a good job of estimating their effects. This could also be tuned on a range of [0.01, 10] as with seasonality_prior_scale.\n",
    "\n",
    "- `seasonality_mode`: Options are [`'additive'`, `'multiplicative'`]. Default is `'additive'`, but many business time series will have multiplicative seasonality. This is best identified just from looking at the time series and seeing if the magnitude of seasonal fluctuations grows with the magnitude of the time series (see the documentation here on multiplicative seasonality), but when that isn't possible, it could be tuned.\n",
    "\n",
    "**Maybe tune?**\n",
    "- `changepoint_range`: This is the proportion of the history in which the trend is allowed to change. This defaults to 0.8, 80% of the history, meaning the model will not fit any trend changes in the last 20% of the time series. This is fairly conservative, to avoid overfitting to trend changes at the very end of the time series where there isn't enough runway left to fit it well. With a human in the loop, this is something that can be identified pretty easily visually: one can pretty clearly see if the forecast is doing a bad job in the last 20%. In a fully-automated setting, it may be beneficial to be less conservative. It likely will not be possible to tune this parameter effectively with cross validation over cutoffs as described above. The ability of the model to generalize from a trend change in the last 10% of the time series will be hard to learn from looking at earlier cutoffs that may not have trend changes in the last 10%. So, this parameter is probably better not tuned, except perhaps over a large number of time series. In that setting, [0.8, 0.95] may be a reasonable range.\n",
    "\n",
    "**Parameters that would likely not be tuned**\n",
    "- `growth`: Options are 'linear' and 'logistic'. This likely will not be tuned; if there is a known saturating point and growth towards that point it will be included and the logistic trend will be used, otherwise it will be linear.\n",
    "\n",
    "- `changepoints`: This is for manually specifying the locations of changepoints. None by default, which automatically places them.\n",
    "\n",
    "- `n_changepoints`: This is the number of automatically placed changepoints. The default of 25 should be plenty to capture the trend changes in a typical time series (at least the type that Prophet would work well on anyway). Rather than increasing or decreasing the number of changepoints, it will likely be more effective to focus on increasing or decreasing the flexibility at those trend changes, which is done with `changepoint_prior_scale`.\n",
    "\n",
    "- `yearly_seasonality`: By default ('auto') this will turn yearly seasonality on if there is a year of data, and off otherwise. Options are ['auto', True, False]. If there is more than a year of data, rather than trying to turn this off during HPO, it will likely be more effective to leave it on and turn down seasonal effects by tuning `seasonality_prior_scale`.\n",
    "\n",
    "- `weekly_seasonality`: Same as for `yearly_seasonality`.\n",
    "\n",
    "- `daily_seasonality`: Same as for `yearly_seasonality`.\n",
    "\n",
    "- `holidays`: This is to pass in a dataframe of specified holidays. The holiday effects would be tuned with `holidays_prior_scale`.\n",
    "\n",
    "- `mcmc_samples`: Whether or not MCMC is used will likely be determined by factors like the length of the time series and the importance of parameter uncertainty (these considerations are described in the documentation).\n",
    "\n",
    "- `interval_width`: Prophet `predict` returns uncertainty intervals for each component, like `yhat_lower` and `yhat_upper` for the forecast `yhat`. These are computed as quantiles of the posterior predictive distribution, and `interval_width` specifies which quantiles to use. The default of 0.8 provides an 80% prediction interval. You could change that to 0.95 if you wanted a 95% interval. This will affect only the uncertainty interval, and will not change the forecast `yhat` at all and so does not need to be tuned.\n",
    "\n",
    "- `uncertainty_samples`: The uncertainty intervals are computed as quantiles from the posterior predictive interval, and the posterior predictive interval is estimated with Monte Carlo sampling. This parameter is the number of samples to use (defaults to 1000). The running time for predict will be linear in this number. Making it smaller will increase the variance (Monte Carlo error) of the uncertainty interval, and making it larger will reduce that variance. So, if the uncertainty estimates seem jagged this could be increased to further smooth them out, but it likely will not need to be changed. As with `interval_width`, this parameter only affects the uncertainty intervals and changing it will not affect in any way the forecast `yhat`; it does not need to be tuned."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}