{"ID":5937670,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T11:34:04.318834241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04245","arxiv_id":"2607.04245","title":"Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series","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.","short_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 mo...","url_abs":"https://arxiv.org/abs/2607.04245","url_pdf":"https://arxiv.org/pdf/2607.04245v1","authors":"[\"Zitao Shuai\",\"Zongzhe Xu\",\"Yuntian Wu\",\"Sirui Li\",\"Tianhong Li\",\"Yuzhe Yang\"]","published":"2026-07-05T11:45:20Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}