performance_metrics: Compute performance metrics from cross-validation results.

In prophet: Automatic Forecasting Procedure

Description

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

Usage

performance_metrics(df, metrics = NULL, rolling_window = 0.1)

Arguments

df	The dataframe returned by cross_validation.
metrics	An array of performance metrics to compute. If not provided, will use c('mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage').
rolling_window	Proportion of data to use in each rolling window for computing the metrics. Should be in [0, 1] to average.

Details

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.

Value

A dataframe with a column for each metric, and column 'horizon'.

prophet documentation built on March 30, 2021, 5:05 p.m.