Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
This addresses a specific challenge in time series analysis for domains with segmented data, offering an incremental improvement by incorporating context and handling label noise.
The paper tackles the problem of segmented time series classification with multiple classes of varying durations (MVD), where existing models ignore temporal dependencies and inconsistent boundary labels, and proposes Con4m, a context-aware consistency learning framework that improves performance by leveraging contextual information and harmonizing label inconsistencies, achieving validated effectiveness across multiple datasets.
Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain Multiple classes with Varying Duration of each class (MVD). Therefore, the characteristics of MVD pose unique challenges for segmented TSC, yet have been largely overlooked by existing works. Specifically, there exists a natural temporal dependency between consecutive instances (segments) to be classified within MVD. However, mainstream TSC models rely on the assumption of independent and identically distributed (i.i.d.), focusing on independently modeling each segment. Additionally, annotators with varying expertise may provide inconsistent boundary labels, leading to unstable performance of noise-free TSC models. To address these challenges, we first formally demonstrate that valuable contextual information enhances the discriminative power of classification instances. Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training. Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD. The source code is available at https://github.com/MrNobodyCali/Con4m.