Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

Main Article Content

Pradeep Mishra
Chellai Fatih
Deepa Rawat
Saswati Sahu
Sagar Anand Pandey
M. Ray
Anurag Dubey
Olawale Monsur Sanusi

Abstract

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

Keywords:
Artificial neural network, ARIMA, COVID-19 forecasting, fuzzy time series, India

Article Details

How to Cite
Mishra, P., Fatih, C., Rawat, D., Sahu, S., Pandey, S. A., Ray, M., Dubey, A., & Sanusi, O. M. (2020). Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models. Annual Research & Review in Biology, 35(9), 46-54. https://doi.org/10.9734/arrb/2020/v35i930270
Section
Original Research Article

References

Chen Y, Liu Q, Guo D. Emerging coronaviruses: Genome structure, replication, andpathogenesis. J Med Virol; 2020.

Paules CI, Marston HD, Fauci AS.Coronavirus infections-More than just thecommon cold. JAMA - J Am Med Assoc; 2020.

World Health Organistation.Coronavirus disease 2019 (COVID-19) Situation Report – 181; 2020.

Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals. 2020;134:109761.

Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals. 2020;109864.

Malavika B, Marimuthu S, Joy M, Nadaraj A, Asirvatham ES,Jeyaseelan L. Forecasting COVID-19 epidemic in India and high incidence states usingSIR and logistic growth models. Clinical Epidemiology and Global Health; 2020.

Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PloS one. 2020;15(3):0231236.

Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting the Spread of the COVID-19 Pandemic in Saudi Arabia Using ARIMA Prediction Model Under Current Public Health Interventions. Journal of Infection and Public Health; 2020.

Rustam F, Reshi AA, Mehmood A, Ullah S, On B, Aslam W, Choi GS. COVID-19 Future Forecasting Using Supervised Machine Learning Models. IEEE Access; 2020.

Sujath R, Chatterjee JM, Hassanien AE. A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment. 2020;1.

Gardner Jr, ES. Exponential smoothing: The state of the art. Journal of forecasting. 1985;4(1):1-28.

Box G, Jenkins Gwilym. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day; 1970.

Xie Y, Zhang Y, Ye Z. Short‐term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Computer‐Aided Civil and Infrastructure Engineering. 2007;22(5):326-334.

Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks. The state of the art. International journal of forecasting. 1998;14(1):35-62.

Khashei M, Bijari M. An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications. 2010;37(1):479-489.

Abbasov AM, Mamedova MH. Application of fuzzy time series to population forecasting, Proceedings of 8th Symposium on Information Technology in Urban and Spatial Planning, Vienna University of Technology, February 25-March1. 2003;545-552.

Singh SR. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation. 2008;79:539-554.

Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks:: The state of the art. International journal of forecasting. 1998;14(1):35-62.

Mandal P, Senjyu T, Funabashi T. Neural network models to predict short-term electricity prices and loads. In 2005 IEEE International Conference on Industrial Technology. 2005;164-169.

Jain A, Kumar AM. Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing. 2007;7(2):585-592.

Price RK, Spitznagel EL, Downey TJ, Meyer DJ, Risk NK, El-Ghazzawy OG. Applying artificial neural network models to clinical decision making. Psychological Assessment. 2000;12(1):40.

Zadeh LA. Fuzzy sets. Information and Control. 1965;8:338–353.

SongQ, Chissom BS. Forecasting enrollments with fuzzy time series- part 1. Fuzzy Sets and Systems. 1993;54:1-9.

Chen SM, Hsu CC. A New method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering. 2004;12:234-244.

Huarng H. Huarng models of fuzzy time series for forecasting. Fuzzy Sets and Systems. 2001;123:369-386.

Jiang PQ, Dong P, Li Lian L.A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction, Appl. Soft Comput. 2017;55:44–62

Mishra P, Fatih C, Niranjan HK, TiwariS, Devi M, Dubey A. Modelling and Forecasting of Milk Production in Chhattisgarh and India. Indian Journal of Animal Research. 2020;54 (7):912-917.