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arXiv · 2607.04245

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series

Zitao Shuai·Zongzhe Xu·Yuntian Wu·Sirui Li·Tianhong Li·Yuzhe Yang
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Abstract

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 generative models from five major families and uncover three key findings: (1) flow-matching models provide strong overall performance across most settings; (2) signal properties matter, with demographic covariates improving longitudinal generation and time-frequency modeling improving high-frequency signal generation; and (3) generated signals have practical utility beyond visual realism, with scaling improving generation quality and synthetic data improving downstream performance. Together, SensorGen establishes a broader understanding of design choices, evaluation protocols, and failure modes in real-world sensor data generation.

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Zitao Shuai, Zongzhe Xu, Yuntian Wu, Sirui Li, Tianhong Li, Yuzhe Yang. 2026-07-05. Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series. https://arxiv.org/abs/2607.04245

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