Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

TL;DR

This paper introduces BTSF, a novel unsupervised time-series representation learning framework that combines iterative bilinear temporal-spectral fusion with global context capturing, significantly improving performance on classification, forecasting, and anomaly detection tasks.

Contribution

The paper proposes a unified unsupervised learning framework that explicitly incorporates spectral information and iteratively refines representations through bilinear fusion, addressing limitations of existing contrastive methods.

Findings

Outperforms state-of-the-art methods on multiple tasks

Effectively captures long-term dependencies in time series

Enhances feature representations with spectral information

Abstract

Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the framework of contrastive learning with the time-based augmentation techniques to sample positives and negatives for contrastive training. Nevertheless, they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context. Besides, they all pay no attention to incorporate the spectral information in feature representation. In this paper, we propose a unified framework, namely Bilinear Temporal-Spectral Fusion (BTSF). Specifically, we firstly utilize the instance-level augmentation with a simple dropout on the entire time series for maximally capturing long-term dependencies. We devise a novel iterative bilinear temporal-spectral fusion to explicitly encode the affinities of abundant time-frequency pairs, and iteratively refines representations in a fusion-and-squeeze manner with Spectrum-to-Time (S2T) and Time-to-Spectrum (T2S) Aggregation modules. We firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection. Experimental results shows that our BTSF consistently significantly outperforms the state-of-the-art methods.