dot-compute_naive_cov: Estimate covariance from naive or seasonal-naive residuals

In bayesRecon: Probabilistic Reconciliation via Conditioning

Estimate covariance from naive or seasonal-naive residuals

Description

Estimates via shrinkage the covariance matrix of the residuals of the naive or seasonal naive forecasts. If the frequency of the time series is > 1, the function chooses between the two methods series-by-series according to a selection criterion. If the frequency is 1 or is not provided, only the naive residuals are used.

Usage

.compute_naive_cov(y_train, freq = NULL, criterion = "RSS")

Arguments

y_train	Multivariate time series object or numeric matrix of historical observations, with dimensions ⁠T x n⁠ (rows are time points, columns are series).
freq	Positive integer seasonal frequency (optional). If not provided and y_train is a multivariate time series, the frequency of the data is used.
criterion	Character string used when freq > 1 to choose residuals. Supported values are "RSS" (default) and "seas-test". "RSS" chooses the method with the lower residual sum of squares (RSS), while "seas-test" uses a statistical test for seasonality (requires forecast package).

Value

A numeric ⁠n x n⁠ shrinkage covariance matrix estimated with schaferStrimmer_cov().

See Also

schaferStrimmer_cov(), reconc_t()

bayesRecon documentation built on March 8, 2026, 9:08 a.m.