FEAT: A general framework for feature-aware multivariate time-series representation learning

Multivariate time-series is complex and uncertain. The overall temporal patterns change dynamically over time, and each feature is often observed to have a unique pattern. Therefore, it is challenging to model a framework that can flexibly learn feature-specific unique patterns as well as dynamically changing temporal patterns simultaneously. We propose a general framework for FEature-Aware multivariate Time-series representation learning, called FEAT. Unlike previous methods that only focus on training the overall temporal dependencies, we focus on training feature-specific as well as feature agnostic representations in a data-driven manner. Specifically, we introduce a feature-wise encoder to explicitly model the feature-specific information and design an element-wise gating layer that learns the influence of feature-specific patterns per dataset in general. FEAT outperforms the benchmark models in average accuracy on 29 UEA multivariate time-series classification datasets and in MSE and MAE on four multivariate time-series forecasting datasets.& COPY; 2023 Published by Elsevier B.V.

Automated summary

Multivariate time-series data is complex and uncertain, with unique patterns for each feature and dynamically changing temporal patterns overall. In this study, a general framework called FEAT is proposed to learn feature-specific and dynamically changing temporal patterns simultaneously. FEAT outperforms benchmark models in terms of accuracy and error on multiple datasets.