A study on the effect of correlated data on predictive capabilities

The purpose of this study is to design a predictive model for a daily quality assurance (QA) system that remains unaffected by specific patterns in correlated time series data. All data were sampled from the measured output factor at specific times over a 5-year period during the daily QA process for a 6 MV photon beam of the Varian linear accelerator (LINAC) system. Before constructing predictive structures, an autocorrelation function (ACF) analysis was conducted to verify the correlation of the given time series data. This study determined the optimal configuration for the autoregressive integrated moving average (ARIMA) and nonlinear autoregressive (NAR) neural network models for prediction. Additionally, it utilized correlated time series data to evaluate its impact on the predictive capability. We then compared the actual QA values to those predicted by the selected ARIMA and NAR models for the sampled daily output. Our findings suggest that while the ARIMA model offers a quick and relatively easy approach without requiring complex computational methods, the NAR model outperforms ARIMA, especially in the context of correlated time series data, demonstrating its real clinical utility as a prediction model. This result reveals that correlations are frequently observed in daily QA data. We concluded that these correlations can substantially influence the accuracy of machine behavior predicted based on historical observations. Consequently, analyzing specific patterns and correlated data is imperative for designing predictive structures.