Time Series Domain Adaptation

Time series domain adaptation focuses on training machine learning models on labeled data from one source to accurately predict on unlabeled data from a different, but related, target domain, addressing the challenge of differing data distributions. Current research emphasizes techniques like adversarial training, optimal transport, and contrastive learning, often implemented within deep learning architectures such as convolutional neural networks, transformers, and variational autoencoders, to align features and mitigate temporal inconsistencies across domains. This field is crucial for improving the generalizability of time series models across diverse real-world applications, including activity recognition, anomaly detection, and forecasting, where data distributions inevitably vary. The development of robust benchmarking suites is also a significant area of focus, aiming to standardize evaluation and facilitate fair comparison of different methods.