{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "block_hidden": 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": { "collapsed": true }, "outputs": [], "source": [ "forecast = Prophet(interval_width=0.95).fit(df).predict(future)" ] }, { "cell_type": "code", "execution_count": 4, "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": "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": {}, "outputs": [], "source": [ "m = Prophet(mcmc_samples=300)\n", "forecast = m.fit(df).predict(future)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "output_hidden": true }, "outputs": [ { "data": { "text/plain": [ "\n", "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\n", "\n", "Gradient evaluation took 8.3e-05 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 0.83 seconds.\n", "Adjust your expectations accordingly!\n", "\n", "\n", "Iteration: 1 / 300 [ 0%] (Warmup)\n", "Iteration: 30 / 300 [ 10%] (Warmup)\n", "Iteration: 60 / 300 [ 20%] (Warmup)\n", "Iteration: 90 / 300 [ 30%] (Warmup)\n", "Iteration: 120 / 300 [ 40%] (Warmup)\n", "Iteration: 150 / 300 [ 50%] (Warmup)\n", "Iteration: 151 / 300 [ 50%] (Sampling)\n", "Iteration: 180 / 300 [ 60%] (Sampling)\n", "Iteration: 210 / 300 [ 70%] (Sampling)\n", "Iteration: 240 / 300 [ 80%] (Sampling)\n", "Iteration: 270 / 300 [ 90%] (Sampling)\n", "Iteration: 300 / 300 [100%] (Sampling)\n", "\n", " Elapsed Time: 1.70863 seconds (Warm-up)\n", " 1.71609 seconds (Sampling)\n", " 3.42472 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 2).\n", "\n", "Gradient evaluation took 4.8e-05 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.\n", "Adjust your expectations accordingly!\n", "\n", "\n", "Iteration: 1 / 300 [ 0%] (Warmup)\n", "Iteration: 30 / 300 [ 10%] (Warmup)\n", "Iteration: 60 / 300 [ 20%] (Warmup)\n", "Iteration: 90 / 300 [ 30%] (Warmup)\n", "Iteration: 120 / 300 [ 40%] (Warmup)\n", "Iteration: 150 / 300 [ 50%] (Warmup)\n", "Iteration: 151 / 300 [ 50%] (Sampling)\n", "Iteration: 180 / 300 [ 60%] (Sampling)\n", "Iteration: 210 / 300 [ 70%] (Sampling)\n", "Iteration: 240 / 300 [ 80%] (Sampling)\n", "Iteration: 270 / 300 [ 90%] (Sampling)\n", "Iteration: 300 / 300 [100%] (Sampling)\n", "\n", " Elapsed Time: 1.85037 seconds (Warm-up)\n", " 2.36446 seconds (Sampling)\n", " 4.21483 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\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", "Iteration: 30 / 300 [ 10%] (Warmup)\n", "Iteration: 60 / 300 [ 20%] (Warmup)\n", "Iteration: 90 / 300 [ 30%] (Warmup)\n", "Iteration: 120 / 300 [ 40%] (Warmup)\n", "Iteration: 150 / 300 [ 50%] (Warmup)\n", "Iteration: 151 / 300 [ 50%] (Sampling)\n", "Iteration: 180 / 300 [ 60%] (Sampling)\n", "Iteration: 210 / 300 [ 70%] (Sampling)\n", "Iteration: 240 / 300 [ 80%] (Sampling)\n", "Iteration: 270 / 300 [ 90%] (Sampling)\n", "Iteration: 300 / 300 [100%] (Sampling)\n", "\n", " Elapsed Time: 1.64101 seconds (Warm-up)\n", " 1.61855 seconds (Sampling)\n", " 3.25956 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet' NOW (CHAIN 4).\n", "\n", "Gradient evaluation took 4.8e-05 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.\n", "Adjust your expectations accordingly!\n", "\n", "\n", "Iteration: 1 / 300 [ 0%] (Warmup)\n", "Iteration: 30 / 300 [ 10%] (Warmup)\n", "Iteration: 60 / 300 [ 20%] (Warmup)\n", "Iteration: 90 / 300 [ 30%] (Warmup)\n", "Iteration: 120 / 300 [ 40%] (Warmup)\n", "Iteration: 150 / 300 [ 50%] (Warmup)\n", "Iteration: 151 / 300 [ 50%] (Sampling)\n", "Iteration: 180 / 300 [ 60%] (Sampling)\n", "Iteration: 210 / 300 [ 70%] (Sampling)\n", "Iteration: 240 / 300 [ 80%] (Sampling)\n", "Iteration: 270 / 300 [ 90%] (Sampling)\n", "Iteration: 300 / 300 [100%] (Sampling)\n", "\n", " Elapsed Time: 1.7447 seconds (Warm-up)\n", " 2.42569 seconds (Sampling)\n", " 4.17039 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": "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": 6, "metadata": { "output_hidden": true }, "outputs": [ { "data": { "image/png": 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CLlHzmqN+hvmyxjBjmQLJbKG8vq8zHuLr7143K0kkU/DXD6qKQiKkEzY0nu2b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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.plot_components(forecast);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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B6dVLKpXMl66eyO29IS7+UhHebJABOK1lWDAY1Gq1w7cIfwGefvrpBQsWLF68+MUXXzQY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\nZTKBd2qeQ04DIaTEoCoxqPgh7v5I/MhA4GBf4M0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ppLa7eprkGa7qdhjYFJ5SmC9evEgI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\nAABJRU5ErkJggg==\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 9 -h 6 -u in\n", "prophet_plot_components(m, 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 }