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Bidirectional consistency with temporal-aware for semi-supervised time series classification.

Han Liu¹, Fengbin Zhang¹, Xunhua Huang¹

¹School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 11, 2024

Summary

This summary is machine-generated.

This study introduces a new semi-supervised learning framework for time series classification. It improves feature separation by learning temporal representations, leading to more discriminative class boundaries.

Keywords:

Bidirectional consistency Contrastive learning Semi-supervised learning Time series classification

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Area of Science:

Machine Learning
Artificial Intelligence

Background:

Semi-supervised learning (SSL) effectively reduces reliance on labeled data.
Existing SSL methods often overlook temporal dynamics, hindering feature space separation for time series.
Pseudo-labeling is a common technique for leveraging unlabeled time series data.

Purpose of the Study:

To propose a novel semi-supervised time series classification framework, TS-BCT.
To enhance feature space separability by incorporating temporal representations.
To improve the discriminative power of class boundaries in time series classification tasks.

Main Methods:

Developed a semi-supervised Time Series classification framework via Bidirectional Consistency with Temporal-aware (TS-BCT).
Employed time-specific augmentation to create two distinct views of raw time series data.
Utilized pseudo-label-guided contrastive learning with a temporal-aware module to regularize feature distribution.
Implemented a bidirectional consistency strategy integrating pseudo-labels from multiple views.

Main Results:

TS-BCT effectively regularizes feature space distribution by learning temporal representations.
The framework generates discriminative temporal-invariant representations.
Bidirectional consistency strategy leads to well-separated feature spaces and more discriminative class boundaries.
Experimental results on real-world datasets show TS-BCT outperforms existing methods.

Conclusions:

TS-BCT offers an effective approach for semi-supervised time series classification.
Learning temporal representations is crucial for improving feature separation in SSL for time series.
The proposed framework demonstrates superior performance compared to baseline methods.