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

Subin Kim, Euisuk Chung, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number110790
JournalKnowledge-Based Systems
Publication statusPublished - 2023 Oct 9

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) ( NRF-2022R1A2C2005455 ). This work was also supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) ( P0008691 , The Competency Development Program for Industry Specialist).

Publisher Copyright:
© 2023


  • Contrastive learning
  • Gating mechanism
  • Multivariate time-series
  • Representation learning
  • Self-supervised learning

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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