{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "block_hidden": true,
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%matplotlib inline\n",
    "from fbprophet import Prophet\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import logging\n",
    "logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
    "df = df.loc[:180,]  # Limit to first six months\n",
    "m = Prophet()\n",
    "m.fit(df)\n",
    "future = m.make_future_dataframe(periods=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial log joint probability = -2.43365\n",
       "Optimization terminated normally: \n",
       "  Convergence detected: absolute parameter change was below tolerance\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(prophet)\n",
    "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
    "df <- df[1:180,]\n",
    "m <- prophet(df)\n",
    "future <- make_future_dataframe(m, periods=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default Prophet will return uncertainty intervals for the forecast `yhat`. There are several important assumptions behind these uncertainty intervals.\n",
    "\n",
    "There are three sources of uncertainty in the forecast: uncertainty in the trend, uncertainty in the seasonality estimates, and additional observation noise.\n",
    "\n",
    "### Uncertainty in the trend\n",
    "The biggest source of uncertainty in the forecast is the potential for future trend changes. The time series we have seen already in this documentation show clear trend changes in the history. Prophet is able to detect and fit these, but what trend changes should we expect moving forward? It's impossible to know for sure, so we do the most reasonable thing we can, and we assume that the *future will see similar trend changes as the history*. In particular, we assume that the average frequency and magnitude of trend changes in the future will be the same as that which we observe in the history. We project these trend changes forward and by computing their distribution we obtain uncertainty intervals.\n",
    "\n",
    "One property of this way of measuring uncertainty is that allowing higher flexibility in the rate, by increasing `changepoint_prior_scale`, will increase the forecast uncertainty. This is because if we model more rate changes in the history then we will expect more in the future, and makes the uncertainty intervals a useful indicator of overfitting.\n",
    "\n",
    "The width of the uncertainty intervals (by default 80%) can be set using the parameter `interval_width`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial log joint probability = -2.43365\n",
       "Optimization terminated normally: \n",
       "  Convergence detected: absolute parameter change was below tolerance\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, interval.width = 0.95)\n",
    "forecast <- predict(m, future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forecast = Prophet(interval_width=0.95).fit(df).predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, these intervals assume that the future will see the same frequency and magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals.\n",
    "\n",
    "### Uncertainty in seasonality\n",
    "By default Prophet will only return uncertainty in the trend and observation noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this here for the first six months of the Peyton Manning data from the Quickstart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\n",
       "\n",
       "Gradient evaluation took 5.3e-05 seconds\n",
       "1000 transitions using 10 leapfrog steps per transition would take 0.53 seconds.\n",
       "Adjust your expectations accordingly!\n",
       "\n",
       "\n",
       "Iteration:   1 / 300 [  0%]  (Warmup)\n",
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       "Iteration: 240 / 300 [ 80%]  (Sampling)\n",
       "Iteration: 270 / 300 [ 90%]  (Sampling)\n",
       "Iteration: 300 / 300 [100%]  (Sampling)\n",
       "\n",
       " Elapsed Time: 1.61713 seconds (Warm-up)\n",
       "               1.46049 seconds (Sampling)\n",
       "               3.07762 seconds (Total)\n",
       "\n",
       "\n",
       "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 2).\n",
       "\n",
       "Gradient evaluation took 4.9e-05 seconds\n",
       "1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.\n",
       "Adjust your expectations accordingly!\n",
       "\n",
       "\n",
       "Iteration:   1 / 300 [  0%]  (Warmup)\n",
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       "Iteration: 270 / 300 [ 90%]  (Sampling)\n",
       "Iteration: 300 / 300 [100%]  (Sampling)\n",
       "\n",
       " Elapsed Time: 1.56343 seconds (Warm-up)\n",
       "               1.62792 seconds (Sampling)\n",
       "               3.19134 seconds (Total)\n",
       "\n",
       "\n",
       "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\n",
       "\n",
       "Gradient evaluation took 4.9e-05 seconds\n",
       "1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.\n",
       "Adjust your expectations accordingly!\n",
       "\n",
       "\n",
       "Iteration:   1 / 300 [  0%]  (Warmup)\n",
       "Iteration:  30 / 300 [ 10%]  (Warmup)\n",
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       "Iteration: 270 / 300 [ 90%]  (Sampling)\n",
       "Iteration: 300 / 300 [100%]  (Sampling)\n",
       "\n",
       " Elapsed Time: 1.67866 seconds (Warm-up)\n",
       "               1.68797 seconds (Sampling)\n",
       "               3.36663 seconds (Total)\n",
       "\n",
       "\n",
       "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 4).\n",
       "\n",
       "Gradient evaluation took 4.7e-05 seconds\n",
       "1000 transitions using 10 leapfrog steps per transition would take 0.47 seconds.\n",
       "Adjust your expectations accordingly!\n",
       "\n",
       "\n",
       "Iteration:   1 / 300 [  0%]  (Warmup)\n",
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       "Iteration: 270 / 300 [ 90%]  (Sampling)\n",
       "Iteration: 300 / 300 [100%]  (Sampling)\n",
       "\n",
       " Elapsed Time: 1.65952 seconds (Warm-up)\n",
       "               1.51409 seconds (Sampling)\n",
       "               3.17361 seconds (Total)\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, mcmc.samples = 300)\n",
    "forecast <- predict(m, future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = Prophet(mcmc_samples=300)\n",
    "forecast = m.fit(df).predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This replaces the typical MAP estimation with MCMC sampling, and can take much longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the uncertainty in seasonal components when you plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
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h5I477mhra8vJyfnNb34jaTsJIeRgq2tuqXns70shhmEEQZCxJz8dURRFCBnnk3aw1TX0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\nyP8AcOC7SDjB9/gAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 6 -u in\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dKgujKAr1YZNYQOe14TSP7h2ldzxXKSkjhBCidsxZALhx40auvfZa1q9fz/nn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\nQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the raw posterior predictive samples in Python using the method `m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are upstream issues in PyStan for Windows which make MCMC sampling extremely slow. The best choice for MCMC sampling in Windows is to use R, or Python in a Linux VM."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
