Why the study?
Wearable-based heart rate forecasting typically uses ARIMA models, but prior studies showed superiority without optimizing hyperparameters, leaving the predictive contribution of historical heart rate data unclear.
Does historical heart rate data modeled through complex ARIMA specifications improve short-term heart rate forecasting accuracy compared to a simple random walk model?
Does historical heart rate data modeled through complex ARIMA specifications improve short-term heart rate forecasting accuracy compared to a simple random walk model?
Complex univariate autoregressive models do not outperform a simple random walk model for short-term heart rate forecasting, suggesting that historical heart rate data alone is insufficient for developing reliable early warning systems.
Historical HR data alone may not suffice for reliable early warning systems; leaves open whether multivariate models add value.
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 improve forecast accuracy but also provide valuable insights. By leveraging the interpretability of ARIMA, we tune its hyper-parameters within a minute-by-minute forecasting structure to address the central research question: how does historical HR data contribute to generate accurate short-term HR forecasts? Our analysis finds that the random walk model, a special case of ARIMA, consistently performs comparably to, or even better than, more complex ARIMA specifications. This indicates that HR values alone offer limited predictive power for short-term forecasting, casting doubt on the value of further refinement in univariate models for alarm system development. These findings highlight the limitations of univariate HR forecasting in real-time health monitoring. Rather than increasing model complexity, future research might benefit from exploring alternative approaches to improve early warning system capabilities in real-world settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-025-00191-y.
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Sabbata et al. (2025) studied this question.
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