Computes a suite of performance metrics on the output of cross-validation. By default the following metrics are included: 'mse': mean squared error, 'rmse': root mean squared error, 'mae': mean absolute error, 'mape': mean percent error, 'mdape': median percent error, 'smape': symmetric mean absolute percentage error, 'coverage': coverage of the upper and lower intervals
performance_metrics(df, metrics = NULL, rolling_window = 0.1)The dataframe returned by cross_validation.
An array of performance metrics to compute. If not provided, will use c('mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage').
Proportion of data to use in each rolling window for computing the metrics. Should be in [0, 1] to average.
A dataframe with a column for each metric, and column 'horizon'.
A subset of these can be specified by passing a list of names as the `metrics` argument.
Metrics are calculated over a rolling window of cross validation predictions, after sorting by horizon. Averaging is first done within each value of the horizon, and then across horizons as needed to reach the window size. The size of that window (number of simulated forecast points) is determined by the rolling_window argument, which specifies a proportion of simulated forecast points to include in each window. rolling_window=0 will compute it separately for each horizon. The default of rolling_window=0.1 will use 10 rolling_window=1 will compute the metric across all simulated forecast points. The results are set to the right edge of the window.
If rolling_window < 0, then metrics are computed at each datapoint with no averaging (i.e., 'mse' will actually be squared error with no mean).
The output is a dataframe containing column 'horizon' along with columns for each of the metrics computed.