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

2026-07-05

AI Summary

This work addresses the lack of systematic understanding regarding the success and failure mechanisms of generative models on real-world sensor time-series data. The authors propose SensorGen, the first unified framework for generating and evaluating multi-domain, multimodal sensor signals. They conduct a comprehensive benchmark of five prominent generative model families—including flow matching, diffusion, and autoregressive models—across four domains, seven datasets, and twelve signal modalities, introducing novel techniques for time–frequency modeling and covariate integration. Their findings reveal that flow matching models consistently achieve the best overall performance, that signal characteristics substantially influence generation quality, and that high-fidelity synthetic data can significantly enhance downstream task performance, thereby demonstrating its practical utility.

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.

Problem

• generative models
• sensor time series
• real-world data
• systematic evaluation
• signal generation

Innovation

• SensorGen
• flow-matching
• sensor time series
• generative modeling
• synthetic data