plot_cross_validation_metric

The output from fbprophet.diagnostics.cross_validation.

df_cv

Metric name, one of 'mse', 'rmse', 'mae', 'mape', 'coverage'.

metric

Proportion of data to use for rolling average of
metric. In [0, 1]. Defaults to 0.1.

rolling_window

This uses fbprophet.diagnostics.performance_metrics to compute the metrics.
Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.

Implements a procedure for forecasting time series data based on
an additive model where non-linear trends are fit with yearly, weekly, and
daily seasonality, plus holiday effects. It works best with time series
that have strong seasonal effects and several seasons of historical data.
Prophet is robust to missing data and shifts in the trend, and typically
handles outliers well.

Sean Taylor

prophet

Automatic Forecasting Procedure

Ben Letham

plot_cross_validation_metric function

Plot a performance metric vs. forecast horizon from cross validation.
Cross validation produces a collection of out-of-sample model predictions
that can be compared to actual values, at a range of different horizons
(distance from the cutoff). This computes a specified performance metric
for each prediction, and aggregated over a rolling window with horizon. — plot_cross_validation_metric

Plot a performance metric vs. forecast horizon from cross validation.
Cross validation produces a collection of out-of-sample model predictions
that can be compared to actual values, at a range of different horizons
(distance from the cutoff). This computes a specified performance metric
for each prediction, and aggregated over a rolling window with horizon.

Description

Usage

Arguments

Value

Details