A Unified Shape-Aware Foundation Model for Time Series Classification

TL;DR

UniShape is a novel, shape-aware foundation model for time series classification that enhances interpretability and generalization by adaptively aggregating multiscale shapes and jointly learning instance- and shape-level representations.

Contribution

It introduces a unified shape-aware model with a shape-aware adapter and prototype-based pretraining, specifically addressing classification challenges overlooked by forecasting-focused models.

Findings

Achieves state-of-the-art accuracy on 128 UCR datasets.

Demonstrates superior generalization across diverse domains.

Provides interpretable shape-based features for classification.

Abstract

Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap, we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into class tokens, effectively selecting the most relevant subsequence scales to enhance model interpretability. Meanwhile, a prototype-based pretraining module is introduced to jointly learn instance- and shape-level representations, enabling the capture of transferable shape patterns. Pre-trained on a large-scale multi-domain time series dataset comprising 1.89 million samples, UniShape exhibits superior generalization across diverse target domains. Experiments on 128 UCR datasets and 30 additional time series datasets demonstrate that UniShape achieves state-of-the-art classification performance, with interpretability and ablation analyses further validating its effectiveness.