Specify three time series components for the MBSTS model: the generalized linear trend component, the seasonal component, and the cycle component.
tsc.setting(Ytrain, mu, rho, S, vrho, lambda)An object of the SSModel class.
The multivariate time series to be modeled.
A vector of logic values indicating whether to include a local trend for each target series.
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.
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.
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.
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.
Jinwen Qiu qjwsnow_ctw@hotmail.com Ning Ning patricianing@gmail.com
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.