Search papers, labs, and topics across Lattice.
This study evaluates the forecasting capabilities of Time Series Foundation Models (TSFMs) for short-term Heart Rate Variability (HRV) prediction using data from consumer wearables, addressing the challenges posed by fragmented and artifact-rich signals. By introducing a novel variability-preserving imputation method, the researchers enhanced the models' ability to retain essential physiological dynamics, leading to improved forecasting accuracy. The TSFMs significantly outperformed traditional baselines, achieving a Mean Absolute Scaled Error (MASE) between 0.81 and 0.87, with Chronos and TimesFM emerging as the most effective models for clinical applications.
TSFMs can forecast heart rate variability from consumer wearables with unprecedented accuracy, outperforming traditional methods without any fine-tuning.
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 linear interpolation with locally adaptive stochastic noise, retaining physiological dynamics essential for accurate forecasting. The results show that TSFMs outperformed all baselines without fine-tuning, achieving average Mean Absolute Scaled Error (MASE) between 0.81 and 0.87 across TSFMs and both context lengths (32 and 64 time steps), with Chronos and TimesFM as the top models, though MOIRAI showed limited gains over baselines. With up to a 2-hour forecast horizon, the results establish a baseline for TSFMs'performance on a real-world dataset, highlighting domain-specific fine-tuning as a promising direction for clinical deployment.
