Analysis and Prediction of Time-Series Data Heart Rate Variability Using ARIMA Model

Heart rate variability (HRV) is a critical indicator of cardiovascular health and autonomic nervous system function. Accurate analysis and prediction of HRV can significantly aid in early diagnosis and management of cardiovascular diseases. This study leverages time-series data of heart rate measurements to develop and validate an Autoregressive Integrated Moving Average (ARIMA) model for predicting HRV. The dataset includes daily summaries of heart rate metrics, resting heart rate and detailed breakdowns of time spent in various heart rate zones. By performing descriptive statistics, time series analysis and anomaly detection, we aim to identify patterns and trends in the data. The ARIMA model demonstrates robust performance in forecasting short-term HRV, providing valuable insights into potential cardiovascular events. Our findings highlight the model's potential application in clinical practice for enhanced patient monitoring and timely intervention, improving patient outcomes.