FBLG: a simple and effective approach for temporal dependence discovery from time series data

Abstract

Discovering temporal dependence structure from multivariate time series has established its importance in many applications. We observe that when we look in reversed order of time, the temporal dependence structure of the time series is usually preserved after switching the roles of cause and effect. Inspired by this observation, we create a new time series by reversing the time stamps of original time series and combine both time series to improve the performance of temporal dependence recovery. We also provide theoretical justification for the proposed algorithm for several existing time series models. We test our approach on both synthetic and real world datasets. The experimental results confirm that this surprisingly simple approach is indeed effective under various circumstances.