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Cited by 287COVID-19 epidemiological studiesStock Market Forecasting MethodsForecasting Techniques and Applications

Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends

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ArunKumar, K.E., Kalaga, Dinesh V., Mohan Sai Kumar, Ch., Kawaji, Masahiro, Brenza, Timothy M.·2022·Alexandria Engineering Journal
DOI 10.1016/j.aej.2022.01.011PMC PMC9453185
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Abstract

Several machine learning and deep learning models were reported in the literature to forecast COVID-19 but there is no comprehensive report on the comparison between statistical models and deep learning models. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with GRU and LSTM cells) and statistical techniques (ARIMA and SARIMA) to forecast the country-wise cumulative confirmed, recovered, and deaths. The Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells based on Recurrent Neural Networks (RNN), ARIMA and SARIMA models were trained, tested, and optimized to forecast the trends of the COVID-19. We deployed python to optimize the parameters of ARIMA which include (p, d, q) representing autoregressive and moving average terms and parameters of SARIMA model include additional seasonal terms which are denoted by (P, D, Q). Similarly, for LSTM and GRU based RNN models’ parameters (number of layers, hidden size, learning rate and number of epochs) are optimized by deploying PyTorch machine learning framework. The best model was chosen based on the lowest Mean Square Error (MSE) and Root Mean Squared Error (RMSE) values. For most of the time-series data of the countries, deep learning-based models LSTM and GRU outperformed statistical ARIMA and SARIMA models, with an RMSE values that are 40 folds less than that of the ARIMA models. But for some countries statistical (ARIMA, SARIMA) models outperformed deep learning models. Further, we emphasize the importance of various factors such as age, preventive measures and healthcare facilities etc. that play vital role on the rapid spread of COVID-19 pandemic.

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  • LSTM is most suitable for time-series data analysis among deep learning models.

    “According to multiple state-of-the-art studies, the LSTM model is most suitable for time-series data.”

    From Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework (2024) · cites this paper as [42]

    The Mathematical modelling section describes LSTM as a way to overcome limitations of recurrent networks, supporting its suitability for time-series forecasting. But the Conclusions report that LSTM performed best for some countries’ COVID-19 fatality series while GRU performed best for others; the paper does not establish LSTM as the most suitable deep-learning model for time-series data generally. Page numbers are unavailable in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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  1. [20]Orojo O. Sluggish State-Based Neural Networks Provide State-of-the-art Forecasts of Covid-19 Cases International Conference on Applied Intelligence and Informatics 2021 Springer

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Authors
ArunKumar, K. E., Kalaga, D. V., Mohan Sai Kumar, C., Kawaji, M., & Brenza, T. M.
Affiliations
  • South Dakota School of Mines and Technology (US)
  • City College of New York (US)
Corresponding author
  • ArunKumar, K.E.
  • Kalaga, Dinesh V.
  • Brenza, Timothy M.
ORCID
Venue
Alexandria Engineering Journal
Publisher
Elsevier BV
Published
2022-10
Volume
61
Issue
10
Pages
7585-7603
ISSN
1110-0168 (ISSN-L)
DOI
10.1016/j.aej.2022.01.011
PMC
PMC9453185
OpenAlex
W4205495647
Cited by
287
References
45
Citations by year
2026: 53 · 2025: 77 · 2024: 69 · 2023: 63 · 2022: 25
FWCI
34.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Alexandria Engineering Journal (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
COVID-19 forecasting, gated recurrent unit, long short-term memory, ARIMA, seasonal ARIMA, recurrent neural networks, time series forecasting, cumulative confirmed cases, COVID-19 mortality, COVID-19 recovery, model comparison

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