A hybrid EMD-GRNN-PSO in intermittent time-series data for dengue fever forecasting

Abstract

Accurate forecasting of dengue cases number is urgently needed as an early warning system to prevent future outbreaks. However, forecasting dengue fever cases with intermittent data characteristics are still rare. In addition, good forecasting accuracy for intermittent data is also challenging to obtain. A hybrid Empirical Mode Decomposition (EMD), Generalized Regression Neural Network (GRNN), and Particle Swarm Optimization (PSO) were proposed to solve the problem. First, data preprocessing is done to ensure that the data is ready for further processing and has a relationship with the dengue fever case number. Second, the decomposition extracts the non-stationarity and nonlinearity patterns of each predictor variable and transforms them into several intrinsic mode functions (IMFs). Third, using various data training and testing ratios and cross-validation, the IMFs of each predictor variable were trained with GRNN to capture the best model of dengue fever cases forecasting. PSO algorithm is used to find the optimal parameters of GRNN so that the parameter searching process is more efficient and accuracy increases. Finally, to see the robustness and effectiveness of the proposed hybrid approach, the forecasting performance of the proposed hybrid model was assessed on 21 datasets with different intermittent conditions, data periods, geographical conditions, diverse numbers, and ranges of data. This approach also compared the comparative benchmark models, using MSE, MAE, and SMAPE as evaluation indicators. The Diebold–Mariano test and the pairwise sample t-test show that the proposed model is more reliable in handling intermittent data.