SARIMA-ANN hybrid models for forecasts of SARS-CoV-2 contagion in Perú
DOI:
https://doi.org/10.21754/iecos.v22i1.1332Keywords:
Models, Autoregressive Neural Networks, Multilayer Perceptron, Hybrid models NNAR-ARIMA, Hybrid models MLP-ARIMAAbstract
Hybrid ANN-ARIMA models have been built by remodeling, to make the forecasts of the new cases of infections by Covid-19 in Peru, for this the confirmed cases of Covid-19 were extracted and used between the period 06/03/20 until 02/28/21, from the open data platform of the Ministry of Health. The results found indicate that the 02 best models correspond to the multiplicative hybrid model NNAR (27, 1, 6) * ARIMA (3, 0, 2) (1, 0, 1), and to the additive hybrid model NNAR (27, 1, 6) + ARIMA (1, 0, 1), whose values of the mean absolute percentage error (MAPE) differ by only 0.575%, thus providing almost the same forecasts. Considering the average of the MAPE values for the 03 best models of each modeling category, it has been determined that the NNAR-ARIMA hybrid models are better than the MLP-ARIMA hybrid models, that the NNAR + ARIMA additive hybrid models have a superiority of 1.20 % on the multiplicative hybrid models NNAR * ARIMA; while the superiority of the MLP + ARIMA additive hybrid model over the MLP * ARIMA multiplicative hybrid model reaches 2.31%.
Downloads
Download data is not yet available.
References
BENVENUTO D.; GIOVANETTI M.; VASALLO L.; ANGELETTI S.; & CICCOZZI M.2020) Application of the ARIMA model on the Covid 19 epidemic dataset. Data Brief. 2020; 29: 105340. Published 2020 Feb 26. Doi: 10.1016 / j .dib.2020.105340.
BROCKWELL, P. & DAVIS R. (1991) Time Series: Theory and Methods. Colorado State University, second edition, Springer-Verlag, New York Inc.
BOX G.E.P & JENKINS G.M (1970) Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
CEYLAN Z. (2020) Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of the Total Environment 729 (2020) 138817.
CYBENKO G. (1989) Approximation by superpositions of a sigmoid function. Mathematics of Control Signals and Systems 2, 303–314.
DEHESH T; FARD H.A. M.; & DEHESH P. (2020) Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. MedRxiv preprint,
DOI: https://doi.org/10.1101/2020.03.13.200 35345.
DING G. LI X.; JIAO F.; & SHEN Y. (2020) Brief Analysis of the ARIMA model on the Covid 19 in Italy. MedRxiv preprint https://doi.org/10.1101/2020.04.08.20058636
GANINY S. & NISAR O. (2021) Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID‑19) pandemic: an Indian
Scenario. Modeling Earth Systems and Environment. Modeling Earth Systems and Environment. 2021 Jan: 1-12. DOI: 10.1007/s40808-020-01080-6.
GOBIERNO DEL PERU (2021) Datos abiertos Covid-19 Plataforma Nacional Ministerio de Salud-MINSA. https://www.datosabiertos.gob.pe/dataset/casos-positivos-por-covid-19-ministerio-de-salud-minsa
HAMILTON, J. D. (1994) Time Series Analysis. Princeton University Press, Princeton NJ.
HYDMAN R.J. and ATHANASOPOULOS G. (2013) Forecasting: Principles and Practice. Monash University, Australia.
HORNI.K.; STINCHCOMBE, M.; & WHITE H. (1989) Multilayer feed forward networks are universal approximators. Neural Networks 2, 359–366.
HOOKER R. H. (1901) “On the correlation of the marriage-rate with trade." Journal Roy. Stat. Soc., London, vol. 64, p. 485, 1901.
HORNIK K.; STINCHCOMBE M.; & WHITE H. (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks. Neural Networks 3, 551–560.
NIELSEN A. (2020) Practical Time Series Analysis. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, and Sebastopol. USA
PANKRATZ A. (1983) Forecasting with Univariate Box‐Jenkins Models: Concepts and Cases First edition. John Wiley & Sons, Inc.
PERONE G. (2020a) An ARIMA model to forecast the spread and the final size of Covid 19 epidemic in Italy. No. 20/07. HEDG, c/o Department of Economics, University of York,
PERONE G. (2020b) Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. October 2020 https://arxiv.org/ftp/arxiv/papers/2010/2010.11617.pdf
SAFI S.K. & SANUSI I.S. (2021) A hybrid of artificial neural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting. Model Assisted. Statistics and Applications 16 (2021) 25–35 25 DOI 10.3233/MAS-210512 IOS Press.
SHIBATA K. & IKEDA Y.(2009) “Effect of number of hidden neurons on learning in large-scale layered neural networks,” in Proceedings of the ICROS-SICE International Joint Conference 2009 (ICCASSICE ’09), pp. 5008–5013, August 2009.
TRENN S. (2008) “Multilayer perceptrons: approximation order and necessary number of hidden units,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 836–844.
URIEL J.E.(1985) Análisis de series temporales: Modelos ARIMA. Paraninfo Madrid España
WANG L.; ZOU H.; SU J.; LI L.; & CHAUDHRY S. (2013) An ARIMA-ANN Hybrid Model for Time Series Forecasting. Systems Research and Behavioral Science Syst. Res. 30, 244–259 (2013). Research paper.
WEI W.W.S (1990) Time series analysis univariate and multivariate methods. Temple University. First edition Addison-Wesley, Reading, MA.
YULE G.U. (1909) The Applications of the Method of Correlation to Social and Economic Statistics. Journal of the Royal Statistical Society, Vol. 72, No. 4 (Dec., 1909), pp. 721-730
YULE G.U (1927) On a method of investigations periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philos. Trans. Roy. Soc. London Ser. A 226 267-298.
ZHANG G.P. (1998) Linear and nonlinear time series forecasting with artificial neural networks. Ph.D. Dissertation, Kent State University, Kent, OH.
ZHANG G. P.(2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-75.v
ZHANG G.P. & QI M. (2005) Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, pp. 501-514.
BROCKWELL, P. & DAVIS R. (1991) Time Series: Theory and Methods. Colorado State University, second edition, Springer-Verlag, New York Inc.
BOX G.E.P & JENKINS G.M (1970) Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
CEYLAN Z. (2020) Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of the Total Environment 729 (2020) 138817.
CYBENKO G. (1989) Approximation by superpositions of a sigmoid function. Mathematics of Control Signals and Systems 2, 303–314.
DEHESH T; FARD H.A. M.; & DEHESH P. (2020) Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. MedRxiv preprint,
DOI: https://doi.org/10.1101/2020.03.13.200 35345.
DING G. LI X.; JIAO F.; & SHEN Y. (2020) Brief Analysis of the ARIMA model on the Covid 19 in Italy. MedRxiv preprint https://doi.org/10.1101/2020.04.08.20058636
GANINY S. & NISAR O. (2021) Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID‑19) pandemic: an Indian
Scenario. Modeling Earth Systems and Environment. Modeling Earth Systems and Environment. 2021 Jan: 1-12. DOI: 10.1007/s40808-020-01080-6.
GOBIERNO DEL PERU (2021) Datos abiertos Covid-19 Plataforma Nacional Ministerio de Salud-MINSA. https://www.datosabiertos.gob.pe/dataset/casos-positivos-por-covid-19-ministerio-de-salud-minsa
HAMILTON, J. D. (1994) Time Series Analysis. Princeton University Press, Princeton NJ.
HYDMAN R.J. and ATHANASOPOULOS G. (2013) Forecasting: Principles and Practice. Monash University, Australia.
HORNI.K.; STINCHCOMBE, M.; & WHITE H. (1989) Multilayer feed forward networks are universal approximators. Neural Networks 2, 359–366.
HOOKER R. H. (1901) “On the correlation of the marriage-rate with trade." Journal Roy. Stat. Soc., London, vol. 64, p. 485, 1901.
HORNIK K.; STINCHCOMBE M.; & WHITE H. (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks. Neural Networks 3, 551–560.
NIELSEN A. (2020) Practical Time Series Analysis. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, and Sebastopol. USA
PANKRATZ A. (1983) Forecasting with Univariate Box‐Jenkins Models: Concepts and Cases First edition. John Wiley & Sons, Inc.
PERONE G. (2020a) An ARIMA model to forecast the spread and the final size of Covid 19 epidemic in Italy. No. 20/07. HEDG, c/o Department of Economics, University of York,
PERONE G. (2020b) Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. October 2020 https://arxiv.org/ftp/arxiv/papers/2010/2010.11617.pdf
SAFI S.K. & SANUSI I.S. (2021) A hybrid of artificial neural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting. Model Assisted. Statistics and Applications 16 (2021) 25–35 25 DOI 10.3233/MAS-210512 IOS Press.
SHIBATA K. & IKEDA Y.(2009) “Effect of number of hidden neurons on learning in large-scale layered neural networks,” in Proceedings of the ICROS-SICE International Joint Conference 2009 (ICCASSICE ’09), pp. 5008–5013, August 2009.
TRENN S. (2008) “Multilayer perceptrons: approximation order and necessary number of hidden units,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 836–844.
URIEL J.E.(1985) Análisis de series temporales: Modelos ARIMA. Paraninfo Madrid España
WANG L.; ZOU H.; SU J.; LI L.; & CHAUDHRY S. (2013) An ARIMA-ANN Hybrid Model for Time Series Forecasting. Systems Research and Behavioral Science Syst. Res. 30, 244–259 (2013). Research paper.
WEI W.W.S (1990) Time series analysis univariate and multivariate methods. Temple University. First edition Addison-Wesley, Reading, MA.
YULE G.U. (1909) The Applications of the Method of Correlation to Social and Economic Statistics. Journal of the Royal Statistical Society, Vol. 72, No. 4 (Dec., 1909), pp. 721-730
YULE G.U (1927) On a method of investigations periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philos. Trans. Roy. Soc. London Ser. A 226 267-298.
ZHANG G.P. (1998) Linear and nonlinear time series forecasting with artificial neural networks. Ph.D. Dissertation, Kent State University, Kent, OH.
ZHANG G. P.(2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-75.v
ZHANG G.P. & QI M. (2005) Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, pp. 501-514.
Downloads
Published
2021-12-27
How to Cite
Ordoñez Mercado, A. F. (2021). SARIMA-ANN hybrid models for forecasts of SARS-CoV-2 contagion in Perú. Revista IECOS, 22(1), 7–22. https://doi.org/10.21754/iecos.v22i1.1332
Issue
Section
Research Articles
License
Copyright (c) 2021 IECOS Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
CC BY 4.0 DEED Attribution 4.0 International
