Employing stacked ensemble approach for time series forecasting

This manuscript presents a novel stack-based multi-level ensemble model to forecast the future incidences of conjunctivitis disease. Besides predicting the frequency of conjunctivitis, the proposed model also enhances accuracy through the use of the ensemble model. A stacked multi-level ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is carried out on the collected dataset and further evaluated for various performance measures. The result shows that the various error metrics of the proposed ensemble is decreased significantly. Considering the RMSE (Root Mean Square Error) error values, for instance, are reduced by 39.23%, 9.11%, 19.48%, and 17.14% in comparison to Auto-ARIMA, NNAR, ETS, and HW model in that order. This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA, NNAR, ETS, and HW model applied discretely. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.