Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning

TL;DR

This paper introduces Frequency-masked Embedding Inference (FEI), a non-contrastive method for time series representation learning that overcomes contrastive learning limitations by using frequency masking prompts, leading to better generalization.

Contribution

FEI is a novel non-contrastive approach that infers embeddings through frequency masking prompts, eliminating the need for positive and negative pairs in time series learning.

Findings

FEI outperforms contrastive methods on 8 datasets.

FEI improves generalization in classification and regression tasks.

FEI enables continuous semantic modeling for time series.

Abstract

Contrastive learning underpins most current self-supervised time series representation methods. The strategy for constructing positive and negative sample pairs significantly affects the final representation quality. However, due to the continuous nature of time series semantics, the modeling approach of contrastive learning struggles to accommodate the characteristics of time series data. This results in issues such as difficulties in constructing hard negative samples and the potential introduction of inappropriate biases during positive sample construction. Although some recent works have developed several scientific strategies for constructing positive and negative sample pairs with improved effectiveness, they remain constrained by the contrastive learning framework. To fundamentally overcome the limitations of contrastive learning, this paper introduces Frequency-masked Embedding Inference (FEI), a novel non-contrastive method that completely eliminates the need for positive and negative samples. The proposed FEI constructs 2 inference branches based on a prompting strategy: 1) Using frequency masking as prompts to infer the embedding representation of the target series with missing frequency bands in the embedding space, and 2) Using the target series as prompts to infer its frequency masking embedding. In this way, FEI enables continuous semantic relationship modeling for time series. Experiments on 8 widely used time series datasets for classification and regression tasks, using linear evaluation and end-to-end fine-tuning, show that FEI significantly outperforms existing contrastive-based methods in terms of generalization. This study provides new insights into self-supervised representation learning for time series. The code is available at https://github.com/USTBInnovationPark/Frequency-masked-Embedding-Inference.