A Comparative Study between ARIMA Model, Holt-Winters – No Seasonal and Fuzzy Time Series for New Cases of COVID-19 in Algeria

Abdelmounaim Hadjira^1, , Hicham Salhi² and Fadoua El Hafa³

¹Department of Economics, Mohamed Boudiaf University, M’sila, Algeria

²Laboratory of Applied Research in Hydraulics, University of Mustapha Ben Boulaid-Batna 2, Algeria

³Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China

Cite this paper

Abdelmounaim Hadjira, Hicham Salhi and Fadoua El Hafa. A Comparative Study between ARIMA Model, Holt-Winters – No Seasonal and Fuzzy Time Series for New Cases of COVID-19 in Algeria. American Journal of Public Health Research. 2021; 9(6):248-256. doi: 10.12691/AJPHR-9-6-4

Abstract

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

Keywords

Covid-19, ARIMA, fuzzy time series model, Holt-Winter-non-seasonal, Algeria

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med.2020; 382: 1199-1207.
[2]	Aleem A, Akbar Samad AB, Slenker AK. Emerging Variants of SARS-CoV-2 And Novel Therapeutics Against Coronavirus (COVID-19). StatPearls. Treasure Island (FL): StatPearls Publishing LLC.; 2021.
[3]	GILBERT, Marius, PULLANO, Giulia, PINOTTI, Francesco, et al. Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study. The Lancet, 2020, vol. 395, no 10227, p. 871-877.
[4]	SAHAI, Alok Kumar, RATH, Namita, SOOD, Vishal, et al. ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020, vol. 14, no 5, p. 1419-1427.
[5]	YANG, Qiuying, WANG, Jie, MA, Hongli, et al. Research on COVID-19 based on ARIMA modelΔ—Taking Hubei, China as an example to see the epidemic in Italy. Journal of Infection and Public Health, 2020, vol. 13, no 10, p. 1415-1418.
[6]	DUAN, Xingde et ZHANG, Xiaolei. ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data. Data in brief, 2020, vol. 31, p. 105779.
[7]	IBRAHIM, Rauf Rauf et OLADIPO, OLUWAKEMI HANNAH. Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins modeling procedure. medRxiv, 2020.
[8]	FATIH, Chellai, HAMIMES, Ahmed, et MISHRA, Pradeep. A note on Covid-19 Statistics, Strange trend and Forecasting of Total Cases in the most Infected African Countries: An ARIMA and Fuzzy Time Series Approaches. African Journal of Applied Statistics, 2020, vol. 2, no 2, p. 961-975.
[9]	Roser, Max, et al. “Coronavirus pandemic (COVID-19).” Our world in data (2020). https://ourworldindata.org/coronavirus.
[10]	Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, (2014).
[11]	Box, G.E.P. and G.M Jenkins. Time series Analysis Forecasting and Control, Holden-day, San Francisco. (1970).
[12]	Maddala, Gangadharrao S., and In-Moo Kim. “Unit roots, cointegration, and structural change.” (1998).
[13]	Box, George EP, et al. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
[14]	Song, Q. and B.S. Chissom (1993b). Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269-277.
[15]	RANA, A. K. Study on Fuzzy Time Invariant Series Models for Crop Production Forecasting. International Journal of Scientific Research and Reviews, 2019, vol. 8, no 2, p. 3729-3741.
[16]	RAJARATHINAM, A. et THIRUNAVUKKARASU, M. Fuzzy Time Series Modeling for Paddy (Oryza sativa L.) Crop Production. 2013.
[17]	VOVAN, Tai. An improved fuzzy time series forecasting model using variations of data. Fuzzy Optimization and Decision Making, 2019, vol. 18, no 2, p. 151-173.
[18]	Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA.
[19]	Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342.
[20]	Wheelwright, Steven, Spyros Makridakis, and Rob J. Hyndman. Forecasting: methods and applications. John Wiley & Sons, 1998.
[21]	CHEN, Shyi-Ming. Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 1996, vol. 81, no 3, p. 311-319.
[22]	Singh, Shiva Raj. “A computational method of forecasting based on fuzzy time series.” Mathematics and Computers in Simulation 79.3 (2008): 539-554.
[23]	Huarng, H., 2001. Huarng models of fuzzy time series for forecasting. Fuzzy Sets and Systems. 123: 369-386.
[24]	Abbasov, A.M. and Mamedova, M.H., 2003. Application of fuzzy time series to population forecasting, Proceedings of 8th Symposion on Information Technology in Urban and Spatial Planning, Vienna University of Technology, February 25-March1, 545-552.
[25]	Chen, S.M. and Hsu, C.C., 2004. A New method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering, 12: 234-244.