SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain

Ahmar, Ansari Saleh; Val, Eva Boj del

doi:10.1016/j.scitotenv.2020.138883

Cited by 150 publications

(132 citation statements)

References 14 publications

(11 reference statements)

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“…The incorporation between ARIMA and α-Sutte indicator method to forecast the confirmed cases in Spain was proposed by ( Ahmar and del Val, 2020 ). The proposed hybrid approach succeeded to predict the COVID-19 cases with a higher prediction accuracy compared with conventional ARIMA approach.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

112

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Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

112

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“…Benvenuto et al [23] performed ARIMA model on the Johns Hopkins epidemiological data and they found that the spread of virus tends to be slightly decreasing. However, Ahmar and del Val [24] combined the α-Sutte indicator with ARIMA and developed a model to forecast COVID-19 outbreak in Spain. Their combined model presented more accurate forecast compared to the ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

et al. 2020

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Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

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“…In a situation like this, staying at home and lockdown seems to be the only solution at the moment which every economy across the world can afford. However, the lockdown has adversely hit the world economy [7], [8] and the psychological health of the people [9]. Looking at the ever-increasing number of infections, the countries across the world have been extending the lockdown period.…”

Section: Introductionmentioning

confidence: 99%

Prediction of curve flattening time for COVID-19 infected countries using trends from recovered countries

Kumar

2020

Preprint

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The outbreak of Coronavirus disease 2019 (COVID-19) has resulted in a serious pandemic situation which the world is going through since the beginning of the year 2020. Given this, the infected countries are constantly increasing their lockdown period and therefore, experiencing an adverse impact on their socio-economic structure. With these motivations, this paper is an attempt to estimate the time that a country would take more to recover from COVID-19 situation. In this work, for curve fitting, the polynomial model has been used deliberately for easier interpretation of the technique. The methodology captures the characteristic trends of the already flattened curves from recovered countries and utilizes them to estimate the curve flattening time for the infected countries. The approach requires only the minimum set of data, i.e., the number of infected persons per day for a given country.

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SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain

Cited by 150 publications

References 14 publications

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

Prediction of curve flattening time for COVID-19 infected countries using trends from recovered countries

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