Singular Spectrum analysis based hybrid models for emergency ambulance demand time series forecasting

Abstract

Abstract One of the challenges of emergency ambulance demand (EAD) time series prediction lies in their non-stationary nature. We study this important problem and propose two hybrid forecasting models, which combine the Singular Spectrum Analysis (SSA) time-series technique with Autoregressive Integrated Moving Average (ARIMA) parameterized multivariate forecasting. Both daily and hourly time series are studied. The non-stationary time series are decomposed into three eigentriples by SSA: trends, periodic components and residuals. Selection of the group boundary point of the periodic component is a key issue in the SSA method. We use spectrum analysis to compute a threshold for maximum information content of periodic components. ARIMA mean value prediction models are employed to forecast the trends, periodic components and residuals sub-series. Our research compares ARIMA and SSA-based hybrid models by considering the emergency dispatching departure records of six core districts in Guangzhou city from January 1, 2021 to December 31, 2021. Results show that the integrated SSA-ARIMA model performs best. SSA is a very effective pre-processing method for non-stationary time series prediction. The predictive accuracy of using a hybrid model for hourly EAD time series is higher than that for daily ones. Our discussion should be useful for improving EAD prediction in contexts others than that considered in our research.