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.
This uses fbprophet.diagnostics.performance_metrics to compute the metrics. Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.
plot_cross_validation_metric(df_cv, metric, rolling_window = 0.1)
df_cv |
The output from fbprophet.diagnostics.cross_validation. |
metric |
Metric name, one of 'mse', 'rmse', 'mae', 'mape', 'coverage'. |
rolling_window |
Proportion of data to use for rolling average of metric. In [0, 1]. Defaults to 0.1. |
rolling_window is the proportion of data included in the rolling window of aggregation. The default value of 0.1 means 10 aggregation for computing the metric.
As a concrete example, if metric='mse', then this plot will show the squared error for each cross validation prediction, along with the MSE averaged over rolling windows of 10
A ggplot2 plot.
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