TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series

arXiv:2310.01327v2 Announce Type: replace
Abstract: We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to …

arxiv cs.ai cs.lg faster multivariate series stat.ml time series type