Meta-learning to Address Data Shift in Time Series Classification

• URL: http://arxiv.org/abs/2601.09018v1
• Date: Tue, 13 Jan 2026 22:38:43 GMT
• Title: Meta-learning to Address Data Shift in Time Series Classification
• Authors: Samuel Myren, Nidhi Parikh, Natalie Klein,
• Abstract summary: Traditional deep learning (TDL) models perform well when training and test data share the same distribution.<n>The dynamic nature of real-world data renders TDL models prone to rapid performance degradation, requiring costly relabeling and inefficient retraining.<n>Here, we systematically compare TDL with fine-tuning and optimization-based meta-learning algorithms to assess their ability to address data shift.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Across engineering and scientific domains, traditional deep learning (TDL) models perform well when training and test data share the same distribution. However, the dynamic nature of real-world data, broadly termed \textit{data shift}, renders TDL models prone to rapid performance degradation, requiring costly relabeling and inefficient retraining. Meta-learning, which enables models to adapt quickly to new data with few examples, offers a promising alternative for mitigating these challenges. Here, we systematically compare TDL with fine-tuning and optimization-based meta-learning algorithms to assess their ability to address data shift in time-series classification. We introduce a controlled, task-oriented seismic benchmark (SeisTask) and show that meta-learning typically achieves faster and more stable adaptation with reduced overfitting in data-scarce regimes and smaller model architectures. As data availability and model capacity increase, its advantages diminish, with TDL with fine-tuning performing comparably. Finally, we examine how task diversity influences meta-learning and find that alignment between training and test distributions, rather than diversity alone, drives performance gains. Overall, this work provides a systematic evaluation of when and why meta-learning outperforms TDL under data shift and contributes SeisTask as a benchmark for advancing adaptive learning research in time-series domains.

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