nmfkc.ar: Construct observation and covariate matrices for a vector...

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Construct observation and covariate matrices for a vector autoregressive model

Description

nmfkc.ar generates the observation matrix and covariate matrix corresponding to a specified autoregressive lag order.

If the input Y is a ts object, its time properties are preserved in the "tsp_info" attribute, adjusted for the lag. Additionally, the column names of Y and A are set to the corresponding time points.

Usage

nmfkc.ar(Y, degree = 1, intercept = TRUE)

Arguments

Y	An observation matrix (P x N) or a ts object. If Y is a ts object (typically N x P), it is automatically transposed to match the (P x N) format.
degree	The lag order of the autoregressive model. The default is 1.
intercept	Logical. If TRUE (default), an intercept term is added to the covariate matrix.

Value

A list containing:

Y	Observation matrix (P x N_A) used for NMF. Includes adjusted "tsp_info" attribute and time-based column names.
A	Covariate matrix (R x N_A) constructed according to the specified lag order. Includes adjusted "tsp_info" attribute and time-based column names.
A.columns	Index matrix used to generate A.
degree.max	Maximum lag order.

References

Satoh, K. (2025). Applying non-negative matrix factorization with covariates to multivariate time series data as a vector autoregression model. Japanese Journal of Statistics and Data Science. arXiv:2501.17446. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s42081-025-00314-0")}

See Also

nmfkc, nmfkc.ar.degree.cv, nmfkc.ar.stationarity, nmfkc.ar.DOT

Examples

# Example using AirPassengers (ts object)
d <- AirPassengers
ar_data <- nmfkc.ar(d, degree = 2)
dim(ar_data$Y)
dim(ar_data$A)

# Example using matrix input
Y <- matrix(1:20, nrow = 2)
ar_data <- nmfkc.ar(Y, degree = 1)
ar_data$degree.max

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