{ "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" ] }, "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\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
dsyhatyhat_loweryhat_upperycutoff
02010-02-168.9545828.4628769.4523058.2424932010-02-15
12010-02-178.7209328.2226829.2427888.0080332010-02-15
22010-02-188.6046088.0669209.1449688.0452682010-02-15
32010-02-198.5263798.0291899.0430457.9287662010-02-15
42010-02-208.2682477.7495208.7418477.7450032010-02-15
\n", "" ], "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\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
horizonmsermsemaemapemdapesmapecoverage
037 days0.4933580.7023950.5039770.0583760.0493650.0586770.676565
138 days0.4991120.7064780.5089460.0589510.0491350.0593120.675423
239 days0.5213440.7220420.5150160.0595470.0492250.0600340.672682
340 days0.5286510.7270840.5178730.0598520.0490720.0604090.676336
441 days0.5361490.7322220.5188430.0599270.0491350.0605480.681361
\n", "" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
horizonmase
037 days0.522946
138 days0.528102
239 days0.534401
340 days0.537365
441 days0.538372
\n", "
" ], "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": [ "" ] }, "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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" ] }, "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 - 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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 }