评估时间序列大模型在电子鼻数据上的表现，发现需微调且融合专用模型效果更佳。
Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddings
- 用主流时间序列大模型生成电子鼻数据嵌入表示。
- 微调后性能显著提升，融合专用模型可进一步优化结果。
- 适合关注时序大模型在气体传感中应用的研究者。
受自然语言处理和计算机视觉进展启发，时间序列基础模型（TSFMs）被提出，宣称可在多任务、跨领域（如医疗、气候、制造）中实现强泛化能力。然而其在气体传感数据中的适用性尚未被探索。本文系统评估了近期典型TSFMs（包括Chronos-2和MOMENT）在电子鼻（E-Nose）数据上的表现。重点考察其生成的嵌入是否适用于气体识别与浓度预测。结果表明，直接使用预训练嵌入效果有限，必须通过微调才能获得满意性能；同时，将TSFM嵌入与专用预测模型的表示进行融合，可进一步提升性能，揭示了当前TSFMs在气体传感应用中的潜力与局限。
原文摘要 · Abstract (English)
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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