nmfkc.ar.predict: Forecast future values for NMF-VAR model

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Forecast future values for NMF-VAR model

Description

nmfkc.ar.predict computes multi-step-ahead forecasts for a fitted NMF-VAR model using recursive forecasting.

If the fitted model contains time series property information (from nmfkc.ar), the forecasted values will have appropriate time-based column names.

Usage

nmfkc.ar.predict(x, Y, degree = NULL, n.ahead = 1)

Arguments

x	An object of class nmfkc (the fitted model).
Y	The historical observation matrix used for fitting (or at least the last degree columns).
degree	Optional integer. Lag order (D). If NULL (default), it is inferred from x$A.attr (when available) or from the dimensions of x$C.
n.ahead	Integer (>=1). Number of steps ahead to forecast.

Value

A list with components:

pred	A P \times n.ahead matrix of predicted values. Column names are future time points if time information is available.
time	A numeric vector of future time points corresponding to the columns of pred.

See Also

nmfkc, nmfkc.ar

Examples

# Forecast AirPassengers
d <- AirPassengers
ar_data <- nmfkc.ar(d, degree = 2)
result <- nmfkc(ar_data$Y, ar_data$A, rank = 1)
pred <- nmfkc.ar.predict(result, Y = matrix(d, nrow = 1), degree = 2, n.ahead = 3)
pred$pred

nmfkc documentation built on July 14, 2026, 1:07 a.m.