A Bayesian Approach to Robust Skewed Autoregressive Processes

Maleki, Mohsen; Arellano–Valle, Reinaldo B.; Dey, Dipak K.; Mahmoudi, Mohammad Reza; Jalali, Seyed Mohammad Jafar

doi:10.1177/0008068317732196

Cited by 28 publications

(11 citation statements)

References 38 publications

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“…The autoregressive time series model is a flexible tool to model dependent data and has been used to estimate and forecast many real practical problems, see Refs. [27][28][29][30][31][32][33][34]. In fact, the autoregressive model, determines the probabilistic behavior of the current values X t based on a linear combination of past values ……”

Section: Introductionmentioning

confidence: 99%

Time series modelling to forecast the confirmed and recovered cases of COVID-19

Maleki

Mahmoudi

Wraith

et al. 2020

Travel Medicine and Infectious Disease

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195

144

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Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the "confirmed" and "recovered" COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP-SMN-AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.

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Section: Introductionmentioning

confidence: 99%

Time series modelling to forecast the confirmed and recovered cases of COVID-19

Maleki

Mahmoudi

Wraith

et al. 2020

Travel Medicine and Infectious Disease

Self Cite

195

144

View full text Add to dashboard Cite

show abstract

“…The autoregressive time series model is known as a useful instrument that has been applied to various real real-world problems (Maleki, Arellano-Valle et al 2017, Maleki, Arellano-Valle et al 2017, Maleki, Nematollahi et al 2017, Hajrajabi and Maleki 2019, Zarrin, Maleki et al 2019.…”

Section: Autoregressive Modelmentioning

confidence: 99%

A Review on COVID-19 Forecasting Models

Rahimi

Chen

Gandomi

2020

Preprint

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The Novel coronavirus (COVID-19) has distributed to more than 200 territory worldwide leading to about 24 million confirmed cases as of August 25, 2020. Several models have been released that forecast the outbreak globally. This work presents a review of the most important forecasting models against COVID-19 and shows a short analysis of each one. The work presented in this study possesses two parts. A detailed scientometric analysis was done in the first section that provides an influential tool for describing bibliometric analyses. The analysis was performed on data corresponding to COVID-19 using the Scopus and Web of Science databases. For analysis, keywords and subject areas were addressed while classification of forecasting models, criteria evaluation and comparison of solution approaches were done in the second section of the work. Conclusion and discussion are provided as the final sections of this study.

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“…Time series analysis has been used effectively to model, estimate, forecast and predict real practical problems, see refs. [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] . Symmetry of error's distribution is an essential condition.…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models

Maleki

Mahmoudi

Heydari

et al. 2020

Chaos, Solitons & Fractals

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Coronaviruses are a huge family of viruses that affect neurological, gastrointestinal, hepatic and respiratory systems. The numbers of confirmed cases are increased daily in different countries, especially in Unites State America, Spain, Italy, Germany, China, Iran, South Korea and others. The spread of the COVID-19 has many dangers and needs strict special plans and policies. Therefore, to consider the plans and policies, the predicting and forecasting the future confirmed cases are critical. The time series models are useful to model data that are gathered and indexed by time. Symmetry of error's distribution is an essential condition in classical time series. But there exist cases in the real practical world that assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, the distribution of the error has been considered to be two-piece scale mixtures of normal ( TP – SMN ). The proposed time series models works well than ordinary Gaussian and symmetry models (especially for COVID-19 datasets), and were fitted initially to the historical COVID-19 datasets. Then, the time series that has the best fit to each of the dataset is selected. Finally, the selected models are applied to predict the number of confirmed cases and the death rate of COVID-19 in the world.

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A Bayesian Approach to Robust Skewed Autoregressive Processes

Cited by 28 publications

References 38 publications

Time series modelling to forecast the confirmed and recovered cases of COVID-19

Time series modelling to forecast the confirmed and recovered cases of COVID-19

A Review on COVID-19 Forecasting Models

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models

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