Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries

Singh, Sarbjit; Parmar, Kulwinder Singh; Makkhan, Sidhu Jitendra Singh; Kaur, Jatinder; Peshoria, Shruti; Kumar, Jagdish

doi:10.1016/j.chaos.2020.110086

Cited by 89 publications

(58 citation statements)

References 45 publications

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“…The ARIMA model is composed of three parts. First, the autoregressive part is a linear regression which has a relation between past values and future values of data series; second, the integrated part expresses how many times the data series has to be differenced to obtain a stationary series; and the last one is the moving average part which has a relation between past forecast errors and future values of data series [ 14 ]. These processes can be presented by the models , , , and .…”

Section: Box–jenkin’s Model Developmentmentioning

confidence: 99%

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Atangana

Araz

2021

Adv Differ Equ

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Using the existing collected data from European and African countries, we present a statistical analysis of forecast of the future number of daily deaths and infections up to 10 September 2020. We presented numerous statistical analyses of collected data from both continents using numerous existing statistical theories. Our predictions show the possibility of the second wave of spread in Europe in the worse scenario and an exponential growth in the number of infections in Africa. The projection of statistical analysis leads us to introducing an extended version of the well-blancmange function to further capture the spread with fractal properties. A mathematical model depicting the spread with nine sub-classes is considered, first converted to a stochastic system, where the existence and uniqueness are presented. Then the model is extended to the concept of nonlocal operators; due to nonlinearity, a modified numerical scheme is suggested and used to present numerical simulations. The suggested mathematical model is able to predict two to three waves of the spread in the near future.

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Section: Box–jenkin’s Model Developmentmentioning

confidence: 99%

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Atangana

Araz

2021

Adv Differ Equ

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“…One of the main methods used by the authors is ARIMA, a popular time-series analysis algorithm. It was applied to French data by several authors such as [ 8 , 32 , 46 ]. Table 13 indicates that these works present an NRMSE of 23.5%, 7.1%, and 2.9%, respectively, which can be compared to the NRMSE of 9.9% obtained with the optimistic CNN method we employed.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France

et al. 2021

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This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-19 forecast developed by our research team during the first French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts of this disease, we conceived models for multiple indicators: daily or cumulative confirmed cases, hospitalizations, hospitalizations with artificial ventilation, recoveries, and deaths. In spite of the limited data available when the lockdown was declared, we achieved good short-term performances at the national level with a classical CNN for hospitalizations, leading to its integration into a hospitalizations surveillance tool after the lockdown ended. Also, A Temporal Convolutional Network with quantile regression successfully predicted multiple COVID-19 indicators at the national level by using data available at different scales (worldwide, national, regional). The accuracy of the regional predictions was improved by using a hierarchical pre-training scheme, and an efficient parallel implementation allows for quick training of multiple regional models. The resulting set of models represent a powerful tool for short-term COVID-19 forecasting at different geographical scales, complementing the toolboxes used by health organizations in France.

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“…It also indicates that the observed data exhibits trend and heteroscedasticity. In order to achieve stationarity, differencing transformation, a widely used data transform to obtain a stationary time series data is implemented (Singh et al 2020). The ADF test on the first-order differenced time series showed a P-value of 0.01 which confirms its stationarity.…”

Section: Arima Model For Time Series Analysismentioning

confidence: 93%

“…The investigation on the residuals confirms that the built ARIMA (1,1,1) model has a good fit with the actual values. The verification of the model is also done by conducting the Box- Ljung test which provides a statistical evidence of a good fit (Singh et al 2020). The observed P value is 0.836, a value significantly larger than 0.05.…”

Section: Arima Model For Time Series Analysismentioning

confidence: 99%

COVID-19: A Boon for Tropical Solar Parks?: A Time Series Based Analysis and Forecasting of Solar Irradiance

Hariharan

2020

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Solar power parks are renewable large-scale power generation alternatives for fossil fuel-based power stations. The outbreak of novel coronavirus has restricted vehicular movement which has aided in the reduction of environmental pollutants level. In this study, the environmental data retrieved from the world's largest solar park at Pavagada, India shows the variation in solar irradiance in a tropical climate. A robust ARIMA-based model with low root mean square error value of 0.729 has been built to analyze and forecast the solar irradiance levels. The model predicted solar irradiance range of 4-8 kWh/m 2 /day is higher than the previous years which reveals the potential for increment in the photovoltaic panel power generation in the solar power plants.

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Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries

Cited by 89 publications

References 45 publications

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France

COVID-19: A Boon for Tropical Solar Parks?: A Time Series Based Analysis and Forecasting of Solar Irradiance

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