mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-07-24 19:43:41 +00:00
725 lines
276 KiB
Text
725 lines
276 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"block_hidden": true
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},
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"outputs": [],
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"source": [
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"%load_ext rpy2.ipython\n",
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"%matplotlib inline\n",
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"from fbprophet import Prophet\n",
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"import logging\n",
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"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
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"m = Prophet()\n",
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"m.fit(df)\n",
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"future = m.make_future_dataframe(periods=366)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"block_hidden": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: Rcpp\n",
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"\n",
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: rlang\n",
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"\n",
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
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"\n"
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]
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}
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],
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"source": [
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"%%R\n",
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"library(prophet)\n",
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"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
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"m <- prophet(df)\n",
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"future <- make_future_dataframe(m, periods=366)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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 a initial history of 5 years, and a forecast was made on a one year horizon."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"input_hidden": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4d1861d99a414fd19b0ad8d4daf98f76",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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\n",
|
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"text/plain": [
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"<Figure size 720x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import cross_validation\n",
|
|
"df_cv = cross_validation(\n",
|
|
" m, '365 days', initial='1825 days', period='365 days')\n",
|
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"cutoff = df_cv['cutoff'].unique()[0]\n",
|
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"df_cv = df_cv[df_cv['cutoff'].values == cutoff]\n",
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"\n",
|
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"fig = plt.figure(facecolor='w', figsize=(10, 6))\n",
|
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"ax = fig.add_subplot(111)\n",
|
|
"ax.plot(m.history['ds'].values, m.history['y'], 'k.')\n",
|
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"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."
|
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"output_hidden": true
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},
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"outputs": [
|
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{
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
|
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Making 11 forecasts with cutoffs between 2010-02-15 and 2015-01-20\n",
|
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"\n"
|
|
]
|
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},
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{
|
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
|
" y ds yhat yhat_lower yhat_upper cutoff\n",
|
|
"1 8.242493 2010-02-16 8.954437 8.451457 9.466432 2010-02-15\n",
|
|
"2 8.008033 2010-02-17 8.720815 8.240268 9.199979 2010-02-15\n",
|
|
"3 8.045268 2010-02-18 8.604422 8.098728 9.113440 2010-02-15\n",
|
|
"4 7.928766 2010-02-19 8.526214 7.995282 8.998441 2010-02-15\n",
|
|
"5 7.745003 2010-02-20 8.268029 7.765918 8.794452 2010-02-15\n",
|
|
"6 7.866339 2010-02-21 8.599200 8.057087 9.092473 2010-02-15\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%R\n",
|
|
"df.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\n",
|
|
"head(df.cv)"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"model_id": "da408f9d6cf940f3ab28dcc7114e629c",
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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},
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\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",
|
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" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2010-02-16</td>\n",
|
|
" <td>8.956828</td>\n",
|
|
" <td>8.479812</td>\n",
|
|
" <td>9.450908</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>8.723230</td>\n",
|
|
" <td>8.213162</td>\n",
|
|
" <td>9.217637</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>8.607021</td>\n",
|
|
" <td>8.119864</td>\n",
|
|
" <td>9.066214</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>8.528870</td>\n",
|
|
" <td>8.088676</td>\n",
|
|
" <td>9.024842</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>8.270872</td>\n",
|
|
" <td>7.740251</td>\n",
|
|
" <td>8.760655</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 8.956828 8.479812 9.450908 8.242493 2010-02-15\n",
|
|
"1 2010-02-17 8.723230 8.213162 9.217637 8.008033 2010-02-15\n",
|
|
"2 2010-02-18 8.607021 8.119864 9.066214 8.045268 2010-02-15\n",
|
|
"3 2010-02-19 8.528870 8.088676 9.024842 7.928766 2010-02-15\n",
|
|
"4 2010-02-20 8.270872 7.740251 8.760655 7.745003 2010-02-15"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import cross_validation\n",
|
|
"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')\n",
|
|
"df_cv.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
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"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 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": 6,
|
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"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": 7,
|
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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"text": [
|
|
"\n"
|
|
]
|
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}
|
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],
|
|
"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."
|
|
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 8,
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"metadata": {
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"output_hidden": true
|
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},
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"outputs": [
|
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
|
" horizon mse rmse mae mape coverage\n",
|
|
"1 37 days 0.4961337 0.7043676 0.5067661 0.05873288 0.6740521\n",
|
|
"2 38 days 0.5019566 0.7084890 0.5117524 0.05931071 0.6740521\n",
|
|
"3 39 days 0.5241862 0.7240071 0.5178584 0.05991184 0.6726816\n",
|
|
"4 40 days 0.5314370 0.7289973 0.5207051 0.06021607 0.6763362\n",
|
|
"5 41 days 0.5388498 0.7340639 0.5216663 0.06029145 0.6838739\n",
|
|
"6 42 days 0.5425501 0.7365800 0.5220003 0.06030530 0.6879854\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%R\n",
|
|
"df.p <- performance_metrics(df.cv)\n",
|
|
"head(df.p)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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|
|
" <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>coverage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>37 days</td>\n",
|
|
" <td>0.494800</td>\n",
|
|
" <td>0.703420</td>\n",
|
|
" <td>0.505277</td>\n",
|
|
" <td>0.058540</td>\n",
|
|
" <td>0.050149</td>\n",
|
|
" <td>0.676565</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>38 days</td>\n",
|
|
" <td>0.500706</td>\n",
|
|
" <td>0.707606</td>\n",
|
|
" <td>0.510301</td>\n",
|
|
" <td>0.059120</td>\n",
|
|
" <td>0.049955</td>\n",
|
|
" <td>0.675423</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>39 days</td>\n",
|
|
" <td>0.522967</td>\n",
|
|
" <td>0.723165</td>\n",
|
|
" <td>0.516433</td>\n",
|
|
" <td>0.059724</td>\n",
|
|
" <td>0.050078</td>\n",
|
|
" <td>0.672682</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>40 days</td>\n",
|
|
" <td>0.530259</td>\n",
|
|
" <td>0.728189</td>\n",
|
|
" <td>0.519331</td>\n",
|
|
" <td>0.060033</td>\n",
|
|
" <td>0.049706</td>\n",
|
|
" <td>0.678849</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>41 days</td>\n",
|
|
" <td>0.537736</td>\n",
|
|
" <td>0.733305</td>\n",
|
|
" <td>0.520341</td>\n",
|
|
" <td>0.060114</td>\n",
|
|
" <td>0.049955</td>\n",
|
|
" <td>0.685244</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" horizon mse rmse mae mape mdape coverage\n",
|
|
"0 37 days 0.494800 0.703420 0.505277 0.058540 0.050149 0.676565\n",
|
|
"1 38 days 0.500706 0.707606 0.510301 0.059120 0.049955 0.675423\n",
|
|
"2 39 days 0.522967 0.723165 0.516433 0.059724 0.050078 0.672682\n",
|
|
"3 40 days 0.530259 0.728189 0.519331 0.060033 0.049706 0.678849\n",
|
|
"4 41 days 0.537736 0.733305 0.520341 0.060114 0.049955 0.685244"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import performance_metrics\n",
|
|
"df_p = performance_metrics(df_cv)\n",
|
|
"df_p.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": 10,
|
|
"metadata": {
|
|
"output_hidden": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n"
|
|
},
|
|
"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": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.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 `fbprophet`.\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": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" changepoint_prior_scale seasonality_prior_scale rmse\n",
|
|
"0 0.001 0.01 0.757489\n",
|
|
"1 0.001 0.10 0.745049\n",
|
|
"2 0.001 1.00 0.753315\n",
|
|
"3 0.001 10.00 0.763111\n",
|
|
"4 0.010 0.01 0.536260\n",
|
|
"5 0.010 0.10 0.538103\n",
|
|
"6 0.010 1.00 0.544326\n",
|
|
"7 0.010 10.00 0.520970\n",
|
|
"8 0.100 0.01 0.524669\n",
|
|
"9 0.100 0.10 0.521302\n",
|
|
"10 0.100 1.00 0.520692\n",
|
|
"11 0.100 10.00 0.515338\n",
|
|
"12 0.500 0.01 0.532103\n",
|
|
"13 0.500 0.10 0.528939\n",
|
|
"14 0.500 1.00 0.525256\n",
|
|
"15 0.500 10.00 0.524619\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": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'changepoint_prior_scale': 0.1, 'seasonality_prior_scale': 10.0}\n"
|
|
]
|
|
}
|
|
],
|
|
"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.\n",
|
|
"\n",
|
|
"- `stan_backend`: If both pystan and cmdstanpy backends set up, the backend can be specified. The predictions will be the same, this will not be tuned."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.7.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|