Zero-Shot Forecasting for ECG Time Series Data Using Generative Foundation Models

Forecasting physiological events is essential to pre-ventive care, particularly in the context of cardiac arrhythmia, where timely intervention is crucial to saving lives. However, the complexity of physiological data has historically presented challenges in developing generalizable ML methods. In this paper, we evaluated the zero-shot performance of the recent foundation models, including Chronos, TimesFM, and Lag-Llama, on the well benchmarked MIT-BIH ECG data. Finetuning was also performed on Chronos models of varying sizes. LSTM was used as the baseline comparison. We observed that while these generative models can in some cases outperform traditional methods, the computational expense is limiting for practical applications. Our benchmarks revealed that Chronos and Lag-Llama perform more than 600 times slower than the LSTM model. However, we observe that TimesFM performs up to 179 times faster than a similarly sized Chronos model, showing that there is a substantial potential for optimization. Finetuning Chronos models yielded an average reduction of 47% in RMSE across 64 time steps, underscoring the importance of pretraining on domain relevant datasets.