ts_reg_mv: Target-Centered Multivariate Regression Base

In tspredit: Time Series Prediction with Integrated Tuning

Target-Centered Multivariate Regression Base

Description

Base class for singular multivariate time-series models that operate on aligned observations (sw = 1).

Usage

ts_reg_mv(models_x = NULL)

Arguments

models_x

Optional named list with one univariate model per auxiliary variable. These models are used to generate future paths for ⁠x1, ..., xn⁠ when the target-centered model needs auxiliary forecasts along the horizon. They are not required when future auxiliary values are supplied directly at prediction time.

Details

ts_reg_mv() is the multivariate counterpart of the raw-series branch of tspredit.

It is intended for models that consume aligned multivariate observations directly, without first materializing explicit lagged windows in ts_data_mv(..., sw > 1).

This branch is appropriate when the multivariate relationship is naturally expressed at the aligned-observation level, for example:

• target-centered linear regression over synchronized covariates

• ARIMA with external regressors (ARIMAX)

• vector autoregression over the whole system

The design remains target-centered:

• the multivariate object still declares one target variable y

• predict() returns the forecast of y by default

• descendants may also expose the forecast path of the remaining variables when return_all = TRUE

Typical descendants are:

• ts_arimax(): target-centered dynamic regression with ARIMA errors

• ts_lm_mv(): target-centered multivariate linear regression

• ts_var(): vector autoregression, still exposed through a target-centered interface

The interface keeps a distinguished target variable y, but models may also return the forecast path of the remaining variables when requested.

Value

A ts_reg_mv object.

tspredit documentation built on Sept. 9, 2026, 9:08 a.m.