LogoRA: Local-Global Representation Alignment for Robust Time Series Classification
Primary Area: transfer learning, meta learning, and lifelong learning
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Keywords: Multi-Scale, Feature Alignment, Unsupervised Domain Adaptation, Time Series Classification, Robust Representation Learning
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TL;DR: We introduce a Local-Global Representation Alignment framework, LogoRA, for unsupervised domain adaptation in time series data, effectively extracting and aligning both global and local features, outperforming strong baselines in UDA tasks.
Abstract: Unsupervised domain adaptation (UDA) of time series data aims to teach models to identify consistent patterns across various temporal scenarios, disregarding domain-specific differences, which can maintain their predictive accuracy and effectively adapt to new domains. However, existing UDA methods struggle to adequately extract and align both global and local features in time series data. To address this issue, we propose **Lo**cal-**G**l**o**bal **R**epresentation **A**lignment framework (LogoRA), which employs a two-branch encoder—comprising a multi-scale convolutional branch and a patching transformer branch. The encoder enables the extraction of both local and global representations from time series instances. A fusion module is then introduced to integrate these representations, enhancing domain-invariant feature alignment from multi-scale perspectives. To achieve effective alignment, LogoRA employs strategies like invariant feature learning on the source domain, utilizing triplet loss for fine alignment and dynamic time warping-based feature alignment. Additionally, it reduces source-target domain gaps through adversarial training and per-class prototype alignment. Our evaluations on four time-series datasets demonstrate that LogoRA outperforms strong baselines by up to $12.52$%, showcasing its superiority in time series UDA tasks.
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Submission Number: 4919
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