SensorGen: A Benchmark for Sensor Time Series Generation
yang_yuzhe · x · 2026-07-13
SensorGen is an open, large-scale research initiative for real-world sensor time series generation, aiming to unify fragmented tasks into a single testbed.
It covers:
- 14 generation settings
- 7 real-world datasets
- 12 signal modalities
- 5 generative models
- 4 core capabilities: semantic-to-signal generation, interpolation & prediction, cross-channel translation, and signal editing
The authors summarize a few observations: high-frequency signals rely heavily on time-frequency modeling; long sequences need observational context as time anchors; longitudinal signals benefit from subject-level context; and noisy environments require more stable normalization.
Related event: UCLA Introduces SensorGen Benchmark(3 posts)→
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