  Intensive Longitudinal Data (ILD) is increasingly available to social and
behavioral scientists. With this increased availability come new opportunities
for modeling and predicting complex biological, behavioral, and physiological
phenomena. Despite these new opportunities psychological researchers have not
taken full advantage of promising opportunities inherent to this data, the
potential to forecast psychological processes at the individual level. To
address this gap in the literature we present a novel modeling framework that
addresses a number of topical challenges and open questions in the
psychological literature on modeling dynamic processes. First, how can we model
and forecast ILD when the length of individual time series and the number of
variables collected are roughly equivalent, or when time series lengths are
shorter than what is typically required for time series analyses? Second, how
can we best take advantage of the cross-sectional (between-person) information
inherent to most ILD scenarios while acknowledging individuals differ both
quantitatively (e.g. in parameter magnitude) and qualitatively (e.g. in
structural dynamics)? Despite the acknowledged between-person heterogeneity in
many psychological processes is it possible to leverage group-level information
to support improved forecasting at the individual level? In the remainder of
the manuscript, we attempt to address these and other pressing questions
relevant to the forecasting of multiple-subject ILD.
