Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series

This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this area predict the future behavior of dynamical systems by inferring the evolution function. However, these methods may confront obstacles due to the presence of missing variables, which are usually attributed to challenges in measurement and a partial understanding of the system of interest. To overcome this obstacle, we introduce the autoregressive with slack time series …

behavior cs.lg domain evolution forecasting function future obstacles series slack stat.me stat.ml study systems through time series