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

• URL: http://arxiv.org/abs/2606.27672v1
• Date: Fri, 26 Jun 2026 03:07:44 GMT
• Title: Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings
• Abstract summary: "Time-series foundation models" (TSFMs) have been introduced with the promise of strong generalization across diverse time-series tasks.<n>This paper systematically evaluates recent TSFMs on electronic nose (E-Nose) data.
• Score: 3.5880535198436156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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