Title: An ensemble approach to forecast COVID-19 incidences using linear and non-linear statistical models

Authors: Asmita Mahajan; Nonita Sharma; Firas Husham Almukhtar; Monika Mangla; Krishna Pal Sharma; Rajneesh Rani

Addresses: Department of Computer Science and Engineering, National Institute of Technology, Jalandhar, Punjab, India ' Department of Computer Science and Engineering, National Institute of Technology, Jalandhar, Punjab, India ' Department of Computer Sciences and Information Technology, Catholic University of Erbil, Ankawa, Erbil, Kurdishtan, Iraq ' Department of Computer Engineering, Lokmanya Tilak College of Engineering, Navi Mumbai, India ' Department of Computer Science and Engineering, National Institute of Technology, Jalandhar, Punjab, India ' Department of Computer Science and Engineering, National Institute of Technology, Jalandhar, Punjab, India

Abstract: Coronavirus has infected many countries across the globe and is still spreading. As health sectors are experiencing an unexpected rise in cases each day, researchers are perpetually trying to discover a permanent antidote for the virus. Considering the spread, it becomes imperative to predict the spread of disease to mitigate the spread. This paper proposes a stacked ensemble model to predict occurrences of COVID-19 using Exponential Smoothing (ETS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Network Auto-regression (NNAR) as the base models. Each base model is trained individually on the disease data set, whose regress values are then used to train the Multi-Layer Perceptron (MLP) model. The stacked ensemble model outperforms the base models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results conclude that the ensemble model is competent to forecast future COVID incidences in comparison to other statistical time series models.

Keywords: coronavirus; COVID-19; pandemic; forecasting; ensemble; stacking; auto regressive integrated moving average; exponential smoothing; neural network; multi-layer perceptron.

DOI: 10.1504/IJCAT.2021.120449

International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.415 - 426

Received: 01 Jul 2020
Accepted: 01 Aug 2020
Published online: 21 Jan 2022 *

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