Predicting Daily Confirmed COVID-19 Cases in India: Time Series Analysis (ARIMA)

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Author(s): Sudip Singh (Uttar Pradesh Rajya Vidyut Utpadan Nigam, India)
Copyright: 2021
Pages: 18
Source title: Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
Source Author(s)/Editor(s): Manikant Roy (Lovely Professional University, India)and Lovi Raj Gupta (Lovely Professional University, India)
DOI: 10.4018/978-1-7998-7188-0.ch003

Keywords: Knowledge Management and Library Science / Machine Learning / Medical Information Science Reference / Medical Technologies

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Abstract

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

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