Abstract
Forecast systems related to forecasting infection cases of Covid-19 are based on time series models because they are considered to be highly accurate in forecasting Covid-19 cases due to their accuracy over epidemiological models that are related to forecasting Covid-19 cases. In this paper, we have two tasks. The first task is to improve forecasting and decrease MAPE% errors in forecasting infection cases through the development of the “Epidemic.TA” system. The development of this algorithm will be called the ensembling time series and neural network system (ET-system). The development of the system was completed by adding a cubic smoothing spline model. This system also applies the method of ensembling between these models in the system (neural network autoregression, Box-Cox transformation, ARMA residuals Trend and Seasonality, trigonometric Box-Cox transformation, ARMA residuals Trend and Seasonality, Holt’s linear trend, autoregressive integrated moving average, and cubic smoothing splines). We applied ensembling by using two methods. The first is the aggregation (average) of results from these models, and the second is ensembling by using average weight by using a prioritizer. The prioritizer gives weights to time series models and neural network models and then gets the ensembling model’s average weight and compares the errors between these models to choose the best forecast model. The results of the developed system (ET-system) were more accurate than the “Epidemic.TA.” On the other hand, the second task in this paper is to use the bootstrap aggregating (bagging) methodology for the NNAR model to decrease the error value of the peak of the wave of infection cases.
The work was supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0011. The work was supported by the Ministry of Science and Higher Education of the Russian Federation (government order FENU-2020-0022).
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Notes
- 1.
Here and later, the acronyms of ensembling time series and neural network system (ET-system) are listed at the end of the Introduction section.
References
Abotaleb, M.S.: Predicting Covid-19 cases using some statistical models: An application to the cases reported in China, Italy and USA. Acad. J. Appl. Math. Sci. 6(4), 32–40 (2020). https://doi.org/10.32861/ajams.64.32.40
Makarovskikh, T.A., Abotaleb, M.S.A.: Automatic selection of ARIMA model parameters to forecast Covid-19 infection and death cases. Vestnik Yuzhno-Ural’skogo Gosudarstvennogo Universiteta. Seriya Vychislitelnaya Matematika i Informatika 10(2), 20–37 (2021). https://doi.org/10.14529/cmse210202
Abotaleb, M.S.A., Makarovskikh, T.A.: Development of algorithms for choosing the best time series models and neural networks to predict Covid-19 cases. Bull. South Ural State Univ. Ser. Comput. Tech. Autom. Control Radio Electron. 21(3), 26–35 (2021). https://doi.org/10.14529/ctcr210303
Roy, S., Bhunia, G.S., Shit, P.K.: Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model. Earth Syst. Environ. 7, 1385–1391 (2021). https://doi.org/10.1007/s40808-020-00890-y
Al-Turaiki, I., Almutlaq, F., Alrasheed, H., Alballa, N.: Empirical evaluation of alternative time-series models for COVID-19 forecasting in Saudi Arabia. Int. J. Environ. Res. Public Health 18(16), 8660 (2021). https://doi.org/10.3390/ijerph18168660
Ahmar, A.S., Boj, E.: Application of neural network time series (NNAR) and ARIMA to forecast infection fatality rate (IFR) of COVID-19 in Brazil. JOIV Int. J. Inf. Vis. 5(1), 8–10 (2021). https://doi.org/10.30630/joiv.5.1.372
Moein, S., Nickaeen, N., Roointan, A., Borhani, N., Heidary, Z., Javanmard, S.H., Ghaisari, J., Gheisari, Y.: Inefficiency of SIR models in forecasting COVID-19 epidemic: A case study of Isfahan. Scientific Reports 11(1), 1–9 (2021). https://doi.org/10.1038/s41598-021-84055-6
Abotaleb, M., Makarovskikh, T.: System for forecasting COVID-19 cases using time-series and neural networks models. In: Engineering Proceedings (Vol. 5(1), p. 46). Multidisciplinary Digital Publishing Institute (2021). https://doi.org/10.3390/engproc2021005046
Talkhi, N., Fatemi, N.A., Ataei, Z., Nooghabi, M.J.: Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods. Biomed. Signal Process. Control 66, 102494 (2021). https://doi.org/10.1016/j.bspc.2021.102494
Gecili, E., Ziady, A., Szczesniak, R.D.: Forecasting COVID-19 confirmed cases, deaths and recoveries: revisiting established time series modeling through novel applications for the USA and Italy. Plos one 16(1), e0244173 (2021). https://doi.org/10.1371/journal.pone.0244173
Rostami-Tabar, B., Rendon-Sanchez, J.F.: Forecasting COVID-19 daily cases using phone call data. Appl. Soft Comput. 100, 106932 (2021). https://doi.org/10.1016/j.asoc.2020.106932
World Health Organization: https://covid19.who.int/info/. Accessed 31 July 2021
Yandex DataLens: https://datalens.yandex.ru/. Accessed 12 Aug 2021
Abotaleb, M., Makarovskikh, T.: “E-System” or ensembling time series and neural network-system (ET-System) for forecasting Covid-19 infection casses. https://github.com/abotalebmostafa11/E-System. Accessed 11 Aug 2021
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Abotaleb, M., Makarovskikh, T. (2023). Development of Algorithm for Forecasting System Software. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis and Forecasting. ITISE 2021. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-14197-3_14
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