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Forecasting of COVID-19 Cases in INDIA Using ARIMA and AR Time-Series Algorithm

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Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

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Abstract

The COVID-19 pandemic has been spreading and affecting worldwide. On 30th January 2020 India reported its first coronavirus confirmed case. The main aim of the proposed work is to devise an algorithm for prediction of Covid-19 cases in India. In this paper, we propose to use time-series algorithms, Autoregressive Integrated Moving Average (ARIMA) and Autoregressive (AR). We have simulated the designed algorithm with the dataset of COVID-19 till 20th February, 2021 for the wave1 and from 01st March, 2021 till 25th September, 2021 we collected data for wave2 and generated 6-days forecasts of confirmed, recovered and death cases. During the 1st wave we observed that there might be another wave 2, after analyzing the wave1 dataset, of coronavirus as result shows that Covid-19 confirmed cases are rising rapidly. Proposed research observations show that the death rate is decreasing, and recovery rate is increasing, one of the possible reasons is herd immunity and vaccination. We are comparing actual cases with forecasting coronavirus cases. ARIMA based models are showing promising results over AR based models. The most difficult part doing this work is to identify parameters due to sudden increase-decrease trend in coronavirus cases. The proposed work reports quality scoring metrics of forecasting for both the models. This will help future researchers to find the best outcome among Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive (AR) based models.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sheth L.U.J. College of Arts and Sir M.V. College of Science and Commerce, Mumbai, Maharashtra, India

    Dilip Prajapati & Mahendra Kanojia

Authors
  1. Dilip Prajapati
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  2. Mahendra Kanojia
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Corresponding authors

Correspondence to Dilip Prajapati or Mahendra Kanojia .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Matieland, South Africa

    Andries Engelbrecht

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Fabio Scotti

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Mumbai, Mumbai, Maharashtra, India

    Pooja Manghirmalani Mishra

  6. University of Calabria (Unical), Rende, Italy

    Giancarlo Fortino

  7. Department of Informatics, Vilnius University, Kaunas, Lithuania

    Virgilijus Sakalauskas

  8. Center for Smart Computing Continuum, Forschung Burgenland, Eisenstadt, Austria

    Sabri Pllana

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Prajapati, D., Kanojia, M. (2022). Forecasting of COVID-19 Cases in INDIA Using ARIMA and AR Time-Series Algorithm. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_33

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