Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification

2024-07-31

Neural Information Processing Systems

AI Summary

Multi-class variable-duration (MVD) segmented time series classification faces two key challenges: (1) neglect of temporal dependencies between adjacent segments and (2) inconsistent annotation boundaries. Method: Departing from the i.i.d. assumption, this work formally proves— for the first time—the discriminative gain conferred by contextual information. We propose a two-level context-prior-driven consistency learning framework integrating: (i) context-aware consistency regularization, (ii) dynamic neighborhood contrastive learning, (iii) soft boundary label smoothing, and (iv) temporal-adaptive feature alignment. Contribution/Results: These components jointly model inter-segment temporal dependencies and mitigate boundary annotation noise. Evaluated on multiple benchmark datasets, our method achieves average accuracy improvements of 3.2%–7.8%, demonstrating significantly enhanced robustness and tolerance to labeling noise.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Addresses challenges in segmented time series classification with varying class durations
• Leverages contextual information to improve discriminative power of classification
• Harmonizes inconsistent boundary labels for stable model training

Innovation

• Context-aware learning for time series classification
• Consistency framework harmonizing inconsistent labels
• Utilizing contextual priors at data and label levels