Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble
International Journal of Systems Science and Applied Mathematics
Volume 5, Issue 2, June 2020, Pages: 20-26
Received: Jul. 4, 2020;
Accepted: Jul. 20, 2020;
Published: Jul. 28, 2020
Views 315 Downloads 67
Otoo Joseph, Department of Statistics and Actuarial Science, University of Ghana, Legon, Accra, Ghana
Bosson-Amedenu Senyefia, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Nyarko Christiana Cynthia, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Osei-Asibey Eunice, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Boateng Ernest Yeboah, Department of Basic Sciences, School of Basic and Biomedical Sciences, University of Health and Allied Sciences, Ho, Ghana
Follow on us
The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.
COVID-19, Coronavirus, Ensemble, Forecasting, Multi-Model, Time Series
To cite this article
Nyarko Christiana Cynthia,
Boateng Ernest Yeboah,
Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble, International Journal of Systems Science and Applied Mathematics.
Vol. 5, No. 2,
2020, pp. 20-26.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Lai S., Ruktanonchai N. W, Zhou L., Prosper O., Luo W., Floyd J. R, Wesolowski A., Santillana M., Zhang C., Du X., Yu H., and Tatem A. J. Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China. medRxiv preprint; 2020.
Anon. National Contingency Plan for COVID-19, The Philippines; 2020.
Luo J. Predictive Monitoring of COVID-19. Data-Driven Innovation Lab Singapore University of Technology and Design; 2020.
Dehesh T. Mardani-Fard H. A, Dehesh P. Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. medRxiv preprint; 2020.
Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175; 2020.
Wang YH. Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach. Expert Systems with Applications 2009; 36: 564–70.
Ostertagova, E., & Ostertag, O. Forecasting Using Simple Exponential Smoothing Method. Acta Electrotechnica et Informatica, 12 (3), 62-66; (2012).
Ferland, R., Latour, A., & Oraichi, D. Integer-valued GARCH process. Journal of Time Series Analysis, 27, 923–942; (2006).
Fokianos, K., Rahbek, A., & Tjøstheim, D. Poisson Autoregression. Journal of the American Statistical Association, 104, 1430–1439; (2009).
Cui, Y., Li, Q., & Zhu, F. Flexible bivariate Poisson integer-valued GARCH model. Annals of the Institute of Statistical Mathematics. doi: 10.1007/s10463-019-00732-4; (2019).
Fong, S. J., Li, G., Dey, N., & Crespo, R. G. (2020). Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. International Journal of Interactive Multimedia and Artificial Intelligence, 6 (1), 132-140. doi: 10.9781/ijimai.2020.02.002.
Araujo, M. B., & New, M. (2006). Ensemble forecasting of species distributions. TRENDS in Ecology and Evolution, 22 (1), 42-47. doi: 10.1016/j.tree.2006.09.010.
Hong, W.-C. (2009). Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. Electrical Power and Energy Systems, 31, 409-417. doi: 10.1016/j.ijepes.2009.03.020.
Xiao, L., Shao, W., Liang, T., & Wang, C. (2016). A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Applied Energy, 167, 135-153. doi: 10.1016/j.apenergy.2016.01.050.