Adaptive Time Encoding for Irregular Multivariate Time-Series Classification

Sangho Lee, Kyeongseo Min, Youngdoo Son, Hyungrok Do

Keywords: Irregular sampling, Time encoding, Attention mechanism, Consistency regularization, Multivariate time-series classification

TL;DR: We propose a novel time encoding approach that learns effective reference points and incorporates temporal and intervariable dependencies to enhance classification performance on irregular multivariate time series.

Abstract: Time series are often irregularly sampled with uneven time intervals. In multivariate cases, such irregularities may lead to misaligned observations across variables and varying observation counts, making it difficult to extract intrinsic patterns and degrading the classification performance of deep learning models. In this study, we propose an adaptive time encoding approach to address the challenge of irregular sampling in multivariate time-series classification. Our approach generates latent representations at learnable reference points that capture missingness patterns in irregular sequences, enhancing classification performance. We also introduce consistency regularization techniques to incorporate intricate temporal and intervariable information into the learned representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance with high computational efficiency in irregular multivariate time-series classification tasks.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 27813