Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models

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.