% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diagnostics.R
\name{performance_metrics}
\alias{performance_metrics}
\title{Compute performance metrics from cross-validation results.}
\usage{
performance_metrics(df, metrics = NULL, rolling_window = 0.1)
}
\arguments{
\item{df}{The dataframe returned by cross_validation.}

\item{metrics}{An array of performance metrics to compute. If not provided,
will use c('mse', 'rmse', 'mae', 'mape', 'coverage').}

\item{rolling_window}{Proportion of data to use in each rolling window for
computing the metrics. Should be in [0, 1].}
}
\value{
A dataframe with a column for each metric, and column 'horizon'.
}
\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
'coverage': coverage of the upper and lower intervals
}
\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. 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 simulated
forecast point (i.e., 'mse' will actually be squared error with no mean).
The default of rolling_window=0.1 will use 10% of the rows in df in each
window. rolling_window=1 will compute the metric across all simulated
forecast points. The results are set to the right edge of the window.

The output is a dataframe containing column 'horizon' along with columns
for each of the metrics computed.
}