R: Plot a performance metric vs. forecast horizon from cross...

plot_cross_validation_metric {prophet} | R Documentation

## 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

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

### Usage

    plot_cross_validation_metric(df_cv, metric, rolling_window = 0.1)

### Arguments

`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.

### Details

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

### Value

A ggplot2 plot.

[Package prophet version 0.6.1 Index]