An Accurate Model for Covid-19 Positive Cases in India by using Traditional ARIMA and Artificial Neural Networks (LSTM and Bi-LSTM)

M Rajendar; lowast; D Mallikarjuna Reddy; V Nagaraju

doi:10.17485/IJST/v17i23.976

Article

An Accurate Model for Covid-19 Positive Cases in India by using Traditional ARIMA and Artificial Neural Networks (LSTM and Bi-LSTM)

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i23.976

Year: 2024, Volume: 17, Issue: 23, Pages: 2421-2429

Original Article

An Accurate Model for Covid-19 Positive Cases in India by using Traditional ARIMA and Artificial Neural Networks (LSTM and Bi-LSTM)

M Rajendar^1,2∗, D Mallikarjuna Reddy³, V Nagaraju⁴

¹Research Scholar, Department of Mathematics, School of Science GITAM: Deemed to be University, Hyderabad, 502329, Telangana, India
²Assistant professor, Department of Mathematics, CMR Technical Campus, Hyderabad, 501401, Telangana, India
³Department of Mathematics, School of Science GITAM: Deemed to be University, Hyderabad, 502329, Telangana, India
⁴Assistant Professor, Department of Mathematics, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India

*Corresponding Author
Email: [email protected]

Received Date:27 March 2024, Accepted Date:11 April 2024, Published Date:04 June 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This study focuses on evaluating the accuracy of models that can be identified by taking the data of COVID-19-positive cases in India. Methods: To build the models by using the procedures, Artificial Neural Networks (ANN) and Auto Regressive Integrated Moving Average (ARIMA). The data has been taken for various time periods of (Covid-19 positive cases) from March 2022 to July 2023; March 2022 to Nov. 2022 and from Nov. 2022 to July 2023. The data was collected from the official website of the World Health Organization (WHO). The traditional ARIMA and Long Short-Term Memory (LSTM) deep learning methods were applied to build models for various time periods. Findings: Model performance is being measured with the error parameter (Root Mean Square Error) RMSE (215.74, 100.36 and 127.81) respectively for all the time periods. LSTM is performing better than the ARIMA with a minimum value of RMSE. Novelty: The study has been done for the time periods of Covid-19 positive cases with the help of LSTM, Bi-LSTM and ARIMA methods. The outcome of these methods gave; LSTM is the accurate and best performance model.

Keywords: ARIMA, Neural Networks, LSTM, RMSE

References

Hasan I, Dhawan P, Rizvi SAM, Dhir S. Data analytics and knowledge management approach for COVID-19 prediction and control. International Journal of Information Technology. 2023;15(2):937–954. Available from: https://dx.doi.org/10.1007/s41870-022-00967-0
Vig V, Kaur A. Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up. International Journal of System Assurance Engineering and Management. 2022;13(6):2920–2933. Available from: https://dx.doi.org/10.1007/s13198-022-01762-7
Chandraid R, Jain A, Chauhan DS. Deep learning via LSTM models for COVID-19 infection forecasting in India. PLoS ONE. 2022;17(1):1–28. Available from: https://doi.org/10.1371/journal.pone.0262708
Xu L, Magar R, Farimani AB. Forecasting COVID-19 new cases using deep learning methods. Computers in Biology and Medicine. 2022;144:1–7. Available from: https://doi.org/10.1016/j.compbiomed.2022.105342
Abbasimehr H, Paki R. Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization. Chaos, Solitons & Fractals. 2021;142:1–14. Available from: https://doi.org/10.1016/j.chaos.2020.110511
Moudhgalya NB, Divi S, Ganesan VA, Sundar SS, Vijayaraghavan V. DeepTrace: A Generic Framework for Time Series Forecasting. In: International Work-Conference on Artificial Neural Networks, Advances in Computational Intelligence. (pp. 139-151) Springer, Cham. 2019.
Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, et al. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in Physics. 2021;27:1–15. Available from: https://dx.doi.org/10.1016/j.rinp.2021.104495
Elsheikh AH, Saba AI, Elaziz MA, Lu S, Shanmugan S, Muthuramalingam T, et al. Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection. 2021;149:223–233. Available from: https://dx.doi.org/10.1016/j.psep.2020.10.048
Painuli D, Mishra D, Bhardwaj S, Aggarwal M. Forecast and prediction of COVID-19 using machine learning. In: Data Science for COVID-19. (pp. 381-397) 2021.
Morais LRdA, Gomes GSdS. Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model. Applied Soft Computing. 2022;126:1–5. Available from: https://dx.doi.org/10.1016/j.asoc.2022.109315
Rahimi I, Chen F, Gandomi AH. A review on COVID-19 forecasting models. Neural Computing and Applications. 2023;35:23671–23681. Available from: https://dx.doi.org/10.1007/s00521-020-05626-8
Aroraa P, Kumar H, Panigrahi BK. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons and Fractals. 2020;139:1–9. Available from: https://doi.org/10.1016/j.chaos.2020.110017
Sunjaya BA, Permai SD, Gunawan AAS. Forecasting of Covid-19 positive cases in Indonesia using long short-term memory (LSTM) Procedia Computer Science. 2023;216:177–185. Available from: https://dx.doi.org/10.1016/j.procs.2022.12.125
Morais LRdA, Gomes GSdS. Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model. Applied Soft Computing. 2022;126:1–5. Available from: https://doi.org/10.1016/j.asoc.2022.109315
Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Developing an artificial neural network for detecting COVID-19 disease. Journal of Education and Health Promotion. 2022;11(1):1–10. Available from: https://journals.lww.com/jehp/fulltext/2022/11000/developing_an_artificial_neural_network_for.2.aspx
Rajendar M, Reddy DM, Nagesh M, Nagaraju V. Progression of COVID-19 Cases in Telangana State by using ARIMA, MLP, ELM and LSTM Prediction Models by Retrospective Confirmation. Indian Journal of Science and Technology. 2024;17(12):1159–1166. Available from: https://doi.org/10.17485/IJST/v17i12.211
Zhang Y, Tang S, Yu G. An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM. Scientific Reports. 2023;13(1):1–12. Available from: https://dx.doi.org/10.1038/s41598-023-33685-z
Wang Y, Yan Z, Wang D, Yang M, Li Z, Gong X, et al. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models. BMC Infectious Diseases. 2022;22(1):1–12. Available from: https://dx.doi.org/10.1186/s12879-022-07472-6
Zhao D, Zhang R, Zhang H, He S. Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models. Scientific Reports. 2022;12(1):1–13. Available from: https://doi.org/10.1038/s41598-022-23154-4
Jin YC, Cao Q, Wang KN, Zhou Y, Cao YP, Wang XY. Prediction of COVID-19 Data Using Improved ARIMA-LSTM Hybrid Forecast Models. IEEE Access. 2023;11:67956–67967. Available from: https://dx.doi.org/10.1109/access.2023.3291999
Sembiring I, Wahyuni SN, Sediyono E. LSTM algorithm optimization for COVID-19 prediction model. Heliyon. 2024;10(4):1–14. Available from: https://dx.doi.org/10.1016/j.heliyon.2024.e26158
Chang TY, Huang CK, Weng CH, Chen JY. Feature-based deep neural network approach for predicting mortality risk in patients with COVID-19. Engineering Applications of Artificial Intelligence. 2023;124:1–11. Available from: https://dx.doi.org/10.1016/j.engappai.2023.106644
Zeroual A, Harrou F, Dairi A, Sun Y. Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals. 2020;140:1–12. Available from: https://dx.doi.org/10.1016/j.chaos.2020.110121
Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals. 2020;135:1–6. Available from: https://doi.org/10.1016/j.chaos.2020.109864
Vega-Márquez B, Rubio-Escudero C, Nepomuceno-Chamorro IA, Arcos-Vargas Á. Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market. Applied Sciences. 2021;11(13):1–19. Available from: https://dx.doi.org/10.3390/app11136097

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© 2024 Rajendar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)