Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings
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Computer Science > Machine Learning
Title:Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings
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. However, their utility for gas-sensing data remains largely unexplored. To address this gap, this paper systematically evaluates recent TSFMs on electronic nose (E-Nose) data. In particular, we investigate whether embeddings produced by representative TSFMs, including Chronos-2 and MOMENT, provide effective representations for gas identification and concentration prediction. Specifically, we show that fine-tuning is necessary to achieve satisfactory performance on E-Nose data, and fusing TSFM embeddings with representations learned by specialized predictive models can further improve the performance, suggesting both the potential and limitations of current TSFMs for gas-sensing applications.
| Comments: | Submitted to IEEE SENSORS 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.27672 [cs.LG] |
| (or arXiv:2606.27672v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27672
arXiv-issued DOI via DataCite (pending registration)
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