ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India

Khan, Farhan Ullah; Gupta, Rajiv

doi:10.1016/j.jnlssr.2020.06.007

Cited by 126 publications

(97 citation statements)

References 3 publications

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“…It can be observed from the results that there is continuous growing of new cases with the number of 7668 cases per day and will reach 127,129 cumulative daily cases within four weeks. In [ 23 ], author also used ARIMA model to predict the expected infected patients in India for the upcoming days. To train the model, they have used the data from January 31, 2020, to March 25, 2020, and to test the model they used the data from March 26, 2020, to April 15, 2020. nonlinear autoregressive (NAR) has been compared with their model and experimental results show the R2 value for NAR and ARIMA are 0.97 and 0.95 respectively.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Saif

Das

Biswas

2021

J. Inst. Eng. India Ser. B

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In India, the first confirmed case of novel corona virus (COVID-19) was discovered on January 30, 2020. The number of confirmed cases is increasing day by day, and it crossed 21,53,010 on August 9, 2020. In this paper, a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed model is based on adaptive neuro-fuzzy inference system (ANFIS) and mutation-based Bees Algorithm (mBA). The meta-heuristic Bees Algorithm (BA) has been modified applying 4 types of mutation, and mutation-based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USA, and the number of confirmed cases in the next 10 days in India has been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results show that the proposed model has achieved better performance in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE). It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data. Similarly, for USA the values are 4468.72, 3082.07, 6.1, and 0.000952 for RMSE, MAE, MAPE, and NRMSE, respectively. Keywords Mutation-based Bees Algorithm (mBA) Á Adaptive neuro-fuzzy inference system (ANFIS) Á COVID-19 Á Forecast Á RMSE Á MAE Á MAPE Á NRMSE Á India & Suparna Biswas

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Section: Related Workmentioning

confidence: 99%

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Saif

Das

Biswas

2021

J. Inst. Eng. India Ser. B

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“…Autoregressive neural network: In [377] , a nonlinear autoregressive neural network is deployed to build a model of the epidemic to predict the behavior of the epidemic. In [378] , Autoregressive integrated moving average neural network is used to predict the pandemic in Italy, Spain an France.…”

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

156

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“…Marques et al [32] proposed an automated medical diagnostic system by utilizing convolutional neural network with using Efficient Net architecture. Khan et al [33] applied an Auto-Regressive Integrated Moving Average (ARIMA) model on the realistic collected data to predict and forecast the affected cases of COVID-19 in future. Authors then compared the accuracy of results with a NAR based solution and found a high level of accuracy in their results.…”

Section: Introductionmentioning

confidence: 99%

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics

Shoaib

Raja

Sabir

et al. 2021

Computer Methods and Programs in Biomedicine

121

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Background: Mathematical modeling of vector-borne diseases and forecasting of epidemics outbreak are global challenges and big point of concern worldwide. The outbreaks depend on different social and demographic factors based on human mobility structured with the help of mathematical models for vector-borne disease transmission. In Dec 2019, an infectious disease is known as “coronavirus” (officially declared as COVID-19 by WHO) emerged in Wuhan (Capital city of Hubei, China) and spread quickly to all over the china with over 50,000 cases including more than 1000 death within a short period of one month. Multimodal modeling of robust dynamics system is a complex, challenging and fast growing area of the research. Objectives: The main objective of this proposed hybrid computing technique are as follows: The innovative design of the NAR-RBFs neural network paradigm is designed to construct the SITR epidemic differential equation (DE) model to ascertain the different features of the spread of COVID-19. The new set of transformations is introduced for nonlinear input to achieve with a higher level of accuracy, stability, and convergence analysis. Methods: Multimodal modeling of robust dynamics system is a complex, challenging and fast growing area of the research. In this research bimodal spread of COVID-19 is investigated with hybrid model based on nonlinear autoregressive with radial base function (NAR-RBFs) neural network for SITR model. Chaotic and stochastic data of the pandemic. A new class of transformation is presented for the system of ordinary differential equation (ODE) for fast convergence and improvement of desired accuracy level. The proposed transformations convert local optimum values to global values before implementation of bimodal paradigm. Results: This suggested NAR-RBFs model is investigated for the bi-module nature of SITR model with additional feature of fragility in modeling of stochastic variation ability for different cases and scenarios with constraints variation. Best agreement of the proposed bimodal paradigm with outstanding numerical solver is confirmed based on statistical results calculated from MSE, RMSE and MAPE with accuracy level based on mean square error up to 1E-25, which further validates the stability and consistence of bimodal proposed model. Conclusions: This computational technique is shown extraordinary results in terms of accuracy and convergence. The outcomes of this study will be useful in forecasting the progression of COVID-19, the influence of several deciding parameters overspread of COVID-19 and can help for planning, monitoring as well as preventing the spread of COVID-19.

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ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India

Cited by 126 publications

References 3 publications

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Applications of artificial intelligence in battling against covid-19: A literature review

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics

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