Real-Time Forecasting from Wearable-Monitored Heart Rate Data Through Autoregressive Models.

ABSTRACT: Heart rate (HR) analysis is of paramount importance in healthcare, particularly for monitoring cardiovascular health, a global concern. The advent of wearable sensors has enabled continuous HR monitoring, with researchers attempting to develop early detection systems by forecasting HR in a univariate fashion. This study analyzes real-world HR time series gathered during participants daily routines to critically assess the predictive power of past HR data in short-term, univariate forecasting. The literature emphasizes a minute-by-minute, univariate forecasting approach, where state-of-the-art predictive models predominantly employ autoregressive integrated moving average (ARIMA). Yet, its superiority has been proved without studying its optimized hyper-parameters, which could not only impr

SUBMITTER: De Sabbata G 

PROVIDER: S-EPMC12037944 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature