Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning

Area of Science:

• Machine Learning
• Data Science
• Time Series Analysis

Background:

• Time series data are crucial in finance, healthcare, and industry but face challenges with IID assumptions and data annotation costs.
• Unsupervised Domain Adaptation (UDA) and Contrastive Learning (CL) are used to address these issues, but current methods have limitations.
• Existing CL-based UDA methods struggle with fixed data augmentation and neglect hard samples, hindering domain adaptation accuracy.

Purpose of the Study:

• To propose a novel time-series UDA algorithm, Adaptive Contrastive Learning Domain Adaptation (ACLDA).
• To address limitations in existing CL-based UDA methods concerning data augmentation and hard sample handling.
• To enhance the accuracy and robustness of cross-domain time series analysis.

Main Methods:

• Developed an adaptive feature enhancement module integrating adaptive sample augmentation and CL for high-quality transferable features.
• Introduced sample-level adaptive weights based on supervised CL for class-level alignment, prioritizing hard samples.
• Utilized comparative experiments on multiple time-series datasets to evaluate ACLDA's performance.

Main Results:

• ACLDA demonstrated superior performance compared to state-of-the-art domain adaptation methods.
• The proposed method achieved higher average accuracy across various time-series datasets.
• ACLDA effectively handles challenges posed by domain shift in time series data.

Conclusions:

• ACLDA offers a more robust solution for cross-domain time series analysis.
• The adaptive feature enhancement and hard sample weighting strategies are key to ACLDA's effectiveness.
• The findings highlight the potential of adaptive contrastive learning for improving UDA in time series applications.