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Predicting the Growth of COVID-19 in Morocco by Adopting an ARIMA Model

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2020) (AI2SD 2020)


The aim of this paper consists in the application of a well-known statistical analysis model that uses time series data, namely Autoregressive Integrated Moving Average (ARIMA) model, in order to forecast and predict the time evolution of the COVID-19 in Morocco. The dataset has been extracted from the Moroccan Health Ministry website, including confirmed and death cases from first February to end August. The evaluation of ARIMA forecasting models is performed using feature engineering for training COVID-19 Moroccan data and using also the Root Mean squared Logarithmic Error (RMSLE) to evaluate and validate the precision of our ARIMA models. The forecasting results of COVID-19 confirmed cases could be reach the value of 198,506 and death cases could be registered the value of 4311 by the end of November.

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  1. Wang, L., et al.: A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence. Int. J. Antimicrobial Agents 55, 105948D (2020)

    Article  Google Scholar 

  2. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020.

  3. Situation reports.

  4. GardaWorld. Retrieved 27 August 2020.

  5. Retrieved: 27 August 2020.

  6. Box, G.E.P., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970)

    Google Scholar 

  7. McKinney, W.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, pp. 56–61 (2010)

    Google Scholar 

  8. Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)

    Google Scholar 

  9. Brownlee, J .: 2017 Introduction to time series forecasting with python: how to prepare data and develop models to predict the future

    Google Scholar 

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Correspondence to Mohamed Amine Rguibi .

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Rguibi, M.A., Madani, A., Moussa, N. (2022). Predicting the Growth of COVID-19 in Morocco by Adopting an ARIMA Model. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). AI2SD 2020. Advances in Intelligent Systems and Computing, vol 1417. Springer, Cham.

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