Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world systems are observed instead as continuous, high-dimensional, and noisy sensor time series. Existing generative modeling of sensor data, however, remains fragmented across modalities, datasets, and task formulations, limiting a systematic understanding of when, how, and why generative models succeed or fail in real-world settings. To address this gap, we introduce SensorGen, a large-scale study of sensor-signal generation spanning 14 settings across 4 domains, 7 datasets, and 12 signal modalities. Leveraging SensorGen, we systematically evaluate...
| Authors | Zitao Shuai, Zongzhe Xu, Yuntian Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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