Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
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TL;DR - Three time-series foundation models forecasted wearable-derived heart rate variability without fine-tuning, outperforming traditional baselines and potentially offering clinicians up to two hours of lead time.
- TimesFM, Chronos, and MOIRAI were evaluated on artifact-rich data from 49 healthy individuals.
- A variability-preserving imputation method combined linear interpolation with locally adaptive stochastic noise.
- The models achieved average MASE scores of 0.81–0.87 across context lengths of 32 and 64 time steps.
- Chronos and TimesFM performed best; MOIRAI offered limited improvement over baselines, suggesting domain-specific fine-tuning may be needed.
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Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Public signals
N/A
TL;DR - Three time-series foundation models forecasted wearable-derived heart rate variability without fine-tuning, outperforming traditional baselines and potentially offering clinicians up to two hours of lead time.
- TimesFM, Chronos, and MOIRAI were evaluated on artifact-rich data from 49 healthy individuals.
- A variability-preserving imputation method combined linear interpolation with locally adaptive stochastic noise.
- The models achieved average MASE scores of 0.81–0.87 across context lengths of 32 and 64 time steps.
- Chronos and TimesFM performed best; MOIRAI offered limited improvement over baselines, suggesting domain-specific fine-tuning may be needed.