Towards Similarity-Aware Time-Series Classification

TL;DR

This paper introduces SimTSC, a novel framework that leverages graph neural networks to incorporate similarity information for improved time-series classification, bridging traditional similarity-based and deep learning approaches.

Contribution

The paper proposes a general graph-based framework for TSC that effectively combines similarity measures with deep learning, demonstrating superior performance on multiple datasets.

Findings

SimTSC outperforms existing methods on UCR datasets.

Incorporating similarity information improves classification accuracy.

The framework is effective in both supervised and semi-supervised settings.

Abstract

We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner. Motivated by the different working mechanisms within these two research lines, we aim to connect them in such a way as to jointly model time-series similarities and learn the representations. This is a challenging task because it is unclear how we should efficiently leverage similarity information. To tackle the challenge, we propose Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and general framework that models similarity information with graph neural networks (GNNs). Specifically, we formulate TSC as a node classification problem in graphs, where the nodes correspond to time-series, and the links correspond to pair-wise similarities. We further design a graph construction strategy and a batch training algorithm with negative sampling to improve training efficiency. We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping (DTW) as the similarity measure. Extensive experiments on the full UCR datasets and several multivariate datasets demonstrate the effectiveness of incorporating similarity information into deep learning models in both supervised and semi-supervised settings. Our code is available at https://github.com/daochenzha/SimTSC