Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model
An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic’s progress was uncertain, an...
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2022
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1244 |
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| _version_ | 1857415062375890944 |
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| author | Chakraborty, Tanujit |
| author2 | Rai, Shesh N. Bhattacharyya, Arinjita |
| author2_role | author author |
| author_facet | Chakraborty, Tanujit Rai, Shesh N. Bhattacharyya, Arinjita |
| author_role | author |
| dc.creator.none.fl_str_mv | Chakraborty, Tanujit Rai, Shesh N. Bhattacharyya, Arinjita |
| dc.date.none.fl_str_mv | 2022-01-13T06:54:57Z 2022-01-13T06:54:57Z 2022 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.1007/s11071-021-07099-3 https://depot.sorbonne.ae/handle/20.500.12458/1244 10.1007/s11071-021-07099-3 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Nonlinear Dynamics https://link.springer.com/article/10.1007%2Fs11071-021-07099-3 0924-090X https://link.springer.com/article/10.1007%2Fs11071-021-07099-3 |
| dc.subject.none.fl_str_mv | COVID-19 Forecasting Theta model Asymptotic stationarity Hybrid model Autoregressive Neural Networks |
| dc.title.none.fl_str_mv | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic’s progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average. |
| id | sorbonner_dca57ba1584257367d32b38b5ed65443 |
| identifier_str_mv | 10.1007/s11071-021-07099-3 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1244 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series modelChakraborty, TanujitRai, Shesh N.Bhattacharyya, ArinjitaCOVID-19 ForecastingTheta modelAsymptotic stationarityHybrid modelAutoregressive Neural NetworksAn unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic’s progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.2022-01-13T06:54:57Z2022-01-13T06:54:57Z2022Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.1007/s11071-021-07099-3https://depot.sorbonne.ae/handle/20.500.12458/124410.1007/s11071-021-07099-3enNonlinear Dynamicshttps://link.springer.com/article/10.1007%2Fs11071-021-07099-30924-090Xhttps://link.springer.com/article/10.1007%2Fs11071-021-07099-3oai:depot.sorbonne.ae:20.500.12458/12442023-12-05T06:09:17Z |
| spellingShingle | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model Chakraborty, Tanujit COVID-19 Forecasting Theta model Asymptotic stationarity Hybrid model Autoregressive Neural Networks |
| title | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| title_full | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| title_fullStr | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| title_full_unstemmed | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| title_short | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| title_sort | Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model |
| topic | COVID-19 Forecasting Theta model Asymptotic stationarity Hybrid model Autoregressive Neural Networks |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1244 |