arXiv Machine Learning June 29, 2026 By Taeyeong Choi, Mohammed Kamruzzaman

Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings

Read the original on arXiv Machine Learning →

arXiv:2606. 27672v1 Announce Type: new Abstract: Inspired by advances in natural language processing and computer vision, "time-series foundation models" (TSFMs) have recently been introduced with the promise of strong generalization across diverse time-series tasks, including forecasting, classification, and anomaly detection, as well as across domains such as healthcare, climate science, and manufacturing.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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