tsc.setting {mbsts}

Specification of time series components

Description

Specify three time series components for the MBSTS model: the generalized linear trend component, the seasonal component, and the cycle component.

Usage

tsc.setting(Ytrain, mu, rho, S, vrho, lambda)

Arguments

Ytrain

The multivariate time series to be modeled.

mu

A vector of logic values indicating whether to include a local trend for each target series.

rho

A vector of numerical values taking values in [0,1], describing the learning rates at which the local trend is updated for each target series. The value 0 in the j-th entry indicates that the j-th target series does not include slope of trend.

S

A vector of integer values representing the number of seasons to be modeled for each target series. The value 0 in the j-th entry indicates that the j-th target series does not include the seasonal component.

vrho

A vector of numerical values taking values in [0,1], describing a damping factor for each target series. The value 0 in the j-th entry indicates that the j-th target series does not include the cycle component.

lambda

A vector of numerical values, whose entries equal to 2\pi/q with q being a period such that 0<\lambda<\pi, describing the frequency.

Value

An object of the SSModel class.

Author(s)

Jinwen Qiu [e-mail] Ning Ning [e-mail]

References

Qiu, Jammalamadaka and Ning (2018), Multivariate Bayesian Structural Time Series Model, Journal of Machine Learning Research 19.68: 1-33.

Ning and Qiu (2021), The mbsts package: Multivariate Bayesian Structural Time Series Models in R.

Jammalamadaka, Qiu and Ning (2019), Predicting a Stock Portfolio with the Multivariate Bayesian Structural Time Series Model: Do News or Emotions Matter?, International Journal of Artificial Intelligence, Vol. 17, Number 2.