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A dynamical system produces a dependent multivariate sequence calleddynamical time series, developed with an evolution function. As variables inthe dynamical time series at the current time-point usually depend on the wholevariables in the previous time-point, existing studies forecast the variablesat the future time-point by estimating the evolution function. However, somevariables in the dynamical time-series are missing in some practicalsituations. In this study, we propose an autoregressive with slack time series(ARS) model. ARS model involves the simultaneous estimation of the evolutionfunction and the underlying missing variables as a slack time series, with theaid of the time-invariance and linearity of the dynamical system. This studyempirically demonstrates the effectiveness of the proposed ARS model.

Forecasting of the development of a partially-observed dynamical time series with the aid of time-invariance and linearity