Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series

Representational learning of time series plays a crucial role in various fields. However, existing time-series models do not perform well in representation learning. These models usually focus only on the relationship between variables at the same timestamp or only consider the change of individual variables in time, while failing to effectively integrate the two, which limits their ability to capture complex time dependencies and multivariate interactions. We propose a Bi-Branching F eature I nteraction Representation Learning for Multivariate Time Series (Bi-FI) to address these issues. Specifically, we elaborated a frequency domain analysis branch to address the complex associations between variables that are difficult to visualize in the time domain. In addition, to eliminate the time lag effect, another branch employs the mechanism of variable tokenization for attention to learning intra-variable representations. Ultimately, we interactively fuse the features learned from the two branches to obtain a more comprehensive time series representation. Bi-FI performs well in three time series analysis tasks: long sequence prediction, classification, and anomaly detection. Our code and dataset will be available at https://github.com/wwy8/Bi_FI.