Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events. Consumer wearable devices generate fragmented, artifact-rich HRV signals that challenge conventional forecasting approaches. In this study, we evaluated the forecasting ability of three Time Series Foundation Models (TSFMs), TimesFM, Chronos, and MOIRAI, against traditional baselines (Mean, Exponential Smoothing, and Exponentially Weighted Moving Average) on real-world wearable data collected from 49 healthy individuals. To address data fragmentation, we introduce a variability-preserving imputation method that augments line...
| Authors | Luukas Peräkylä, Fahad Sohrab, Ville Hautamäki et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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