Abstract
The upsurge of the novel coronavirus has spread to many countries and has been declared a pandemic by WHO. It has shaken the most powerful countries across the world like the USA, UK, and has affected economies of various countries. The coronavirus or the 2019-nCoV causes the disease that has been named COVID-19. This disease transmits by inhaling droplets that are expelled by an infected person. It has been affecting people in different ways and has been found to be threatening for the older population or people with comorbidities. It has been seen that the virus 2019-nCoV spreads faster than the two of its antecedents namely severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). No cure or vaccine has been discovered as of now and taking precautions like staying at home are the only possible solutions.
Our study analyzes the current trend of the disease in India and predicts future trends using time series forecasting. The official dataset provided by John Hopkins University through a GitHub repository has been used for the research for the time period of 22 January 2020 to 31 May 2020. The trend in cases, fatalities, and the people who have recovered until the date of 31 May 2020 has been discussed in the paper. It has been seen through the findings that the total number of cases is expected to rise to 2,15,000 by the end of May 2020 i.e. 31 May 2020 as per the AR (Autoregression) model. ARIMA (Autoregressive Integrated Moving Average) model predicts the number of cases to be 2,05,000 until the same date. Actual data has shown that the number of confirmed cases is 1,90,609 as on 31 May 2020 giving a percentage error of 7.57% and 12.85% for ARIMA and AR model respectively. Comparison between the findings of the two models has been shown later in the paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ansuj, A.P., Camargo, M., Radharamanan, R., Petry, D.: Sales forecasting using time series and neural networks. Comput. Ind. Eng. 31(1–2), 421–424 (1996)
Arti, M., Bhatnagar, K.: Modeling and predictions for covid 19 spread in India. ResearchGate (2020)
Baud, D., Qi, X., Nielsen-Saines, K., Musso, D., Pomar, L., Favre, G.: Real estimates of mortality following COVID-19 infection. The Lancet Infectious Diseases (2020)
Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., Ciccozzi, M.: Application of the arima model on the Covid-2019 epidemic dataset. Data Brief, 105340 (2020)
Chatfield, C.: Time-Series Forecasting. CRC Press, Boca Raton (2000)
Chatterjee, K., Chatterjee, K., Kumar, A., Shankar, S.: Healthcare impact of Covid-19 epidemic in India: a stochastic mathematical model. Med. J. Armed Forces India (2020)
Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: Arima models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)
Deb, S., Majumdar, M.: A time series method to analyze incidence pattern and estimate reproduction number of covid-19. arXiv preprint arXiv:2003.10655 (2020)
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)
Dehesh, T., Mardani-Fard, H., Dehesh, P.: Forecasting of covid-19 confirmed cases in different countries with arima models. medRxiv (2020)
Gupta, R., Pal, S.K.: Trend analysis and forecasting of covid19 outbreak in India. medRxiv (2020)
John Hopkins university dataset. (2020). https://github.com/CSSEGISandData/COVID-19. Accessed 12 May 2020
Muralidharan, N., Sakthivel, R., Velmurugan, D., Gromiha, M.M.: Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with sars-cov-2 protease against covid-19. J. Biomolecular Struct. Dyn. 56 1–6 (2020)
Pandey, G., Chaudhary, P., Gupta, R., Pal, S.: Seir and regression model based covid-19 outbreak predictions in India. arXiv preprint arXiv:2004.00958 (2020)
Petropoulos, F., Makridakis, S.: Forecasting the novel coronavirus covid-19. PLoS ONE 15(3), (2020)
Ranjan, R.: Predictions for covid-19 outbreak in India using epidemiological models. medRxiv (2020)
Roy, D., Tripathy, S., Kar, S.K., Sharma, N., Verma, S.K., Kaushal, V.: Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during covid-19 pandemic. Asian J. Psychiatry, 102083 (2020)
Sahoo, S., et al.: Self-harm and covid-19 pandemic: an emerging concern–a report of 2 cases from India. Asian J. Psychiatry (2020)
Singh, R., Adhikari, R.: Age-structured impact of social distancing on the covid-19 epidemic in India. arXiv preprint arXiv:2003.12055 (2020)
Singhal, T.: A review of coronavirus disease-2019 (covid-19). The Indian J. Pediatrics, 1–6 (2020)
Tanne, J.H., Hayasaki, E., Zastrow, M., Pulla, P., Smith, P., Rada, A.G.: Covid-19: how doctors and healthcare systems are tackling coronavirus worldwide. BMJ, 368 (2020)
Tay, F.E., Cao, L.: Application of support vector machines in financial time series forecasting. Omega, 29(4), 309–317 (2001)
Tomar, A., Gupta, N.: Prediction for the spread of covid-19 in India and effectiveness of preventive measures. Sci. Total Environ. 138762 (2020)
Vellingiri, B., et al.: Covid-19: a promising cure for the global panic. Sci. Total Environ. 138277 (2020)
W.H.O.: Coronavirus disease 2019 (Covid19): situation report (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sobti, P., Nayyar, A., Nagrath, P. (2021). Time Series Forecasting for Coronavirus (COVID-19). In: Singh, P.K., Veselov, G., Vyatkin, V., Pljonkin, A., Dodero, J.M., Kumar, Y. (eds) Futuristic Trends in Network and Communication Technologies. FTNCT 2020. Communications in Computer and Information Science, vol 1395. Springer, Singapore. https://doi.org/10.1007/978-981-16-1480-4_27
Download citation
DOI: https://doi.org/10.1007/978-981-16-1480-4_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1479-8
Online ISBN: 978-981-16-1480-4
eBook Packages: Computer ScienceComputer Science (R0)