TY - JOUR
T1 - Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19
T2 - methodology, evaluation, and case study in SAARC countries
AU - Sardar, Iqra
AU - Akbar, Muhammad Azeem
AU - Leiva, Víctor
AU - Alsanad, Ahmed
AU - Mishra, Pradeep
N1 - Funding Information:
The authors also thank the financial support provided by the Deanship of Scientific Research, King Saud University. This research was partially funded by FONDECYT grant number 1200525 (V. Leiva) from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.
AB - Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.
KW - Artificial intelligence
KW - Facebook Prophet algorithm
KW - GLM
KW - R software
KW - SARS-CoV-2
KW - South Asian Association for Regional Cooperation countries
KW - Time-series models
UR - http://www.scopus.com/inward/record.url?scp=85139451818&partnerID=8YFLogxK
U2 - 10.1007/s00477-022-02307-x
DO - 10.1007/s00477-022-02307-x
M3 - Article
AN - SCOPUS:85139451818
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
SN - 1436-3240
ER -