{ "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_peyton_manning.csv')\n", "df['y'] = np.log(df['y'])\n", "m = Prophet()\n", "m.fit(df)\n", "future = m.make_future_dataframe(periods=366)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "block_hidden": true }, "outputs": [ { "data": { "text/plain": [ "Initial log joint probability = -19.4685\n", "Optimization terminated normally: \n", " Convergence detected: relative gradient magnitude is below tolerance\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(prophet)\n", "df <- read.csv('../examples/example_wp_peyton_manning.csv')\n", "df$y <- log(df$y)\n", "m <- prophet(df)\n", "future <- make_future_dataframe(m, periods=366)" ] }, { "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 = -19.4685\n", "Optimization terminated normally: \n", " Convergence detected: relative gradient magnitude is 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 Peyton Manning data from the Quickstart:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = Prophet(mcmc_samples=300)\n", "forecast = m.fit(df).predict(future)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "output_hidden": true }, "outputs": [ { "data": { "text/plain": [ "\n", "SAMPLING FOR MODEL 'prophet_linear_growth' NOW (CHAIN 1).\n", "\n", "Gradient evaluation took 0.001006 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 10.06 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: 72.8198 seconds (Warm-up)\n", " 110.177 seconds (Sampling)\n", " 182.997 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet_linear_growth' NOW (CHAIN 2).\n", "\n", "Gradient evaluation took 0.000731 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 7.31 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: 61.1471 seconds (Warm-up)\n", " 101.032 seconds (Sampling)\n", " 162.179 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet_linear_growth' NOW (CHAIN 3).\n", "\n", "Gradient evaluation took 0.00076 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 7.6 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: 68.4866 seconds (Warm-up)\n", " 103.735 seconds (Sampling)\n", " 172.222 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'prophet_linear_growth' NOW (CHAIN 4).\n", "\n", "Gradient evaluation took 0.000714 seconds\n", "1000 transitions using 10 leapfrog steps per transition would take 7.14 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: 79.6159 seconds (Warm-up)\n", " 107.5 seconds (Sampling)\n", " 187.116 seconds (Total)\n", "\n", "\r", "|======================================================|100% ~0 s remaining " ] }, "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 takes much longer - think 10 minutes instead of 10 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": { "image/png": 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808cLO3rpCiWxGrScVO3g/HnVzCqx/8shLY0kYTPqhpY1+eezKz8LOaOSlBUS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Bcvgz9JOovIAFCKiWSrnguKU2h0B9ne4GRvi5dI9PTnoHX6QuxudvPkuip84Si/+tpI9BoV7b4Q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.plot_components(forecast);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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vuY8i12OWSCQkSfr6Fn4nXQ5UmP7xfV1WnHjBtakpv+yvXPhcGfZPYcUGi9eL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CkA+oHIY6nNpLZnapQv51c/Mz5iTIQQQSoD92b/HPPRo0eXL18+depUOv3PFTIyMt544w0XFgz8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\nqkQSCiYaSreYOzo65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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 9 -h 9 -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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }