Impact of statistical approach on time-series models for forecasting COVID-19

Authors

  • Shobhana Kashyap * Department of Computer Science and Engineering, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India. https://orcid.org/0000-0001-7351-2769
  • Avtar Singh Department of Computer Science and Engineering, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India.

https://doi.org/10.22105/thi.v1i1.18

Abstract

For several decades, time-series forecasting has been an engaging research area. It is an essential domain of Machine Learning (ML) that is mainly ignored. It is necessary because prediction problems have a time feature, which makes time series problems more difficult to tackle. Forecasting of many applications such as weather, sales, ECG patterns and even COVID-19 spreads are possible with time series techniques. Inspired by these applications, many scholars have worked on effective forecasting techniques. This paper presents a comparative study of the time series models implemented on India’s real-time data of COVID-19. The study aims to estimate the mortality rate of coming 10 days by the interpretation of actual data. Two predictive algorithms, Holt’s Linear Exponential Smoothing (HLES) and Autoregressive Integrated Moving Average (ARIMA) have been applied. To accomplish the objective and check the model accuracy, two selection criterion methods, Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC), have been used to calculate the lowest values. The results depict that the HLES model has generally outperformed ARIMA. Adding to this, HLES model has good accuracy in forecasting the mortality rate compared to ARIMA. Moreover, if we face similar circumstances again in the future, then the proposed algorithm can be used to prevent the earlier phase of the outbreak.

Keywords:

Time series model, COVID-19, Holt’s linear exponential smoothing, Autoregressive integrated moving average, Akaike information criterion, Root mean square error

References

  1. [1] Singh, S., Parmar, K. S., Kumar, J., & Makkhan, S. J. S. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of covid-19. Chaos, solitons & fractals, 135, 109866. https://doi.org/10.1016/J.CHAOS.2020.109866
  2. [2] Poonia, N., & Azad, S. (2020). Short-term forecasts of covid-19 spread across Indian states until 1 may 2020. https://doi.org/10.20944/preprints202004.0491.v1
  3. [3] Rahimi, I., Chen, F., & Gandomi, A. H. (2023). A review on COVID-19 forecasting models. Neural computing and applications, 35(33), 23671-23681. https://doi.org/10.1007/s00521-020-05626-8
  4. [4] Chakraborty, I., & Maity, P. (2020). COVID-19 outbreak: Migration, effects on society, global environment and prevention. Science of the total environment, 728, 138882. https://doi.org/10.1016/J.SCITOTENV.2020.138882
  5. [5] Kitajima, M., Ahmed, W., Bibby, K., Carducci, A., Gerba, C. P., Hamilton, K. A., ... & Rose, J. B. (2020). SARS-CoV-2 in wastewater: State of the knowledge and research needs. Science of the total environment, 739, 139076. https://doi.org/10.1016/J.SCITOTENV.2020.139076
  6. [6] Fagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., & Huiart, L. (2020). Digital health strategies to fight COVID-19 worldwide: Challenges, recommendations, and a call for papers. Journal of medical internet research, 22(6), e19284. https://www.jmir.org/2020/6/e19284
  7. [7] Choudhari, R. (2020). COVID 19 pandemic: mental health challenges of internal migrant workers of India. Asian journal of psychiatry, 54, 102254. https://doi.org/10.1016/J.AJP.2020.102254
  8. [8] Sharma, H. B., Vanapalli, K. R., Cheela, V. S., Ranjan, V. P., Jaglan, A. K., Dubey, B., … & Bhattacharya, J. (2020). Challenges, opportunities, and innovations for effective solid waste management during and post covid-19 pandemic. Resources, conservation and recycling, 162, 105052. https://doi.org/10.1016/J.RESCONREC.2020.105052
  9. [9] Dhamodharavadhani, S., Rathipriya, R., & Chatterjee, J. M. (2020). COVID-19 mortality rate prediction for India using statistical neural network models. Frontiers in public health, 8(8), 1–12. https://doi.org/10.3389/fpubh.2020.00441
  10. [10] Kumar, S. L., Sarobin, M, V. R., & Anbarasi, L, J. (2021). Predictive analytics of covid-19 pandemic: statistical modelling perspective. Walailak journal of science and technology (WJST), 18(16), 15583. https://doi.org/10.48048/wjst.2021.15583
  11. [11] Kumar, S., Viral, R., Deep, V., Sharma, P., Kumar, M., Mahmud, M., & Stephan, T. (2021). Forecasting major impacts of covid-19 pandemic on country-driven sectors: Challenges, lessons, and future roadmap. Personal and ubiquitous computing, 27, 807-830. https://doi.org/10.1007/s00779-021-01530-7
  12. [12] Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE access, 8, 101489–101499. https://doi.org/10.1109/ACCESS.2020.2997311
  13. [13] Bećirović, E., & Ćosović, M. (2016). Machine learning techniques for short-term load forecasting. 2016 4th international symposium on environmental friendly energies and applications (EFEA) (pp. 1-4). IEEE. https://doi.org/10.1109/EFEA.2016.7748789
  14. [14] Perc, M., Gorišek Miksić, N., Slavinec, M., & Stožer, A. (2020). Forecasting covid-19. Frontiers in physics, 8, 127. https://doi.org/10.3389/fphy.2020.00127
  15. [15] Russo, L., Anastassopoulou, C., Tsakris, A., Bifulco, G. N., Campana, E. F., Toraldo, G., & Siettos, C. (2020). Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach. Plos one, 15(10), e0240649. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240649
  16. [16] Ali, M., Khan, D. M., Aamir, M., Khalil, U., & Khan, Z. (2020). Forecasting COVID-19 in Pakistan. Plos one, 15(11), e0242762. https://doi.org/10.1371/journal.pone.0242762
  17. [17] Barría-Sandoval, C., Ferreira, G., Benz-Parra, K., & López-Flores, P. (2021). Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study. Plos one, 16(4), e0245414. https://doi.org/10.1371/journal.pone.0245414
  18. [18] Lee, D. H., Kim, Y. S., Koh, Y. Y., Song, K. Y., & Chang, I. H. (2021). Forecasting covid-19 confirmed cases using empirical data analysis in korea. Healthcare (Switzerland), 9(3), 1–17. https://doi.org/10.3390/healthcare9030254
  19. [19] Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V. (2020). Time series forecasting of covid-19 using deep learning models: India-USA comparative case study. Chaos, solitons and fractals, 140, 110227. https://doi.org/10.1016/j.chaos.2020.110227
  20. [20] Şahinli, M. A. (2020). Potato price forecasting with holt-winters and arima methods: a case study. American journal of potato research, 97(4), 336–346. https://doi.org/10.1007/s12230-020-09788-y
  21. [21] Chandra, C., Fajrin, A. A., & Eko Suharyanto, C. (2021). Forecasting the items consumption in the hotel storage with the autoregressive integrated moving average method. Engineering, mathematics and computer science (EMACS) journal, 3(1), 13–19. https://doi.org/10.21512/emacsjournal.v3i1.6979
  22. [22] Sharma, V. K., & Nigam, U. (2020). Modeling and forecasting of covid-19 growth curve in India. Transactions of the Indian national academy of engineering, 5(4), 697–710. https://doi.org/10.1007/s41403-020-00165-z
  23. [23] Harous, S., Al Harmoodi, M., & Biri, H. (2019). A comparative study of clustering algorithms for mixed datasets. 2019 amity international conference on artificial intelligence (AICAI) (pp. 484-488). IEEE.
  24. [24] Khan, F. M., & Gupta, R. (2020). ARIMA and nar based prediction model for time series analysis of covid-19 cases in India. Journal of safety science and resilience, 1(1), 12–18. https://doi.org/10.1016/j.jnlssr.2020.06.007
  25. [25] Lydia, M., Kumar, S. S., Selvakumar, A. I., & Kumar, G. E. P. (2016). Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy conversion and management, 112, 115-124. https://doi.org/10.1016/j.enconman.2016.01.007
  26. [26] Katoch, R., & Sidhu, A. (2021). An application of ARIMA model to forecast the dynamics of COVID-19 epidemic in India. Global business review, 0972150920988653. https://journals.sagepub.com/doi/abs/10.1177/0972150920988653
  27. [27] Chu, J. (2021). A statistical analysis of the novel coronavirus (COVID-19) in Italy and Spain. PloS one, 16(3), e0249037. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249037
  28. [28] Li, L., Yang, Z., Dang, Z., Meng, C., Huang, J., Meng, H., … & Shao, Y. (2020). Propagation analysis and prediction of the covid-19. Infectious disease modelling, 5, 282. https://doi.org/10.1016/J.IDM.2020.03.002
  29. [29] Bayham, J., & Fenichel, E. P. (2020). Impact of school closures for COVID-19 on the US health-care workforce and net mortality: A modelling study. The lancet public health, 5(5), e271-e278. https://doi.org/10.1101/2020.03.09.20033415
  30. [30] Hembram, K. P. S. S., & Kumar, J. (2021). Epidemiological study of novel coronavirus (COVID-19): macroscopic and microscopic analysis. International journal of community medicine and public health, 8, 1364-1369.
  31. [31] Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., … & Hemingway, H. (2020). Estimating excess 1-year mortality associated with the covid-19 pandemic according to underlying conditions and age: a population-based cohort study. The lancet, 395(10238), 1715–1725. https://doi.org/10.1016/S0140-6736(20)30854-0
  32. [32] Caccavo, D. (2020). Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. https://doi.org/10.1101/2020.03.19.20039388
  33. [33] Theerthagiri, P., Jacob, I. J., Ruby, A. U., & Vamsidhar, Y. (2020). Prediction of COVID-19 possibilities using KNN classification algorithm. https://doi.org/10.21203/rs.3.rs-70985/v2
  34. [34] Arpaci, I., Huang, S., Al-Emran, M., Al-Kabi, M. N., & Peng, M. (2021). Predicting the covid-19 infection with fourteen clinical features using machine learning classification algorithms. Multimedia tools and applications, 80(8), 11943–11957. https://doi.org/10.1007/s11042-020-10340-7
  35. [35] Chatfield, C. (2000). Multivariate forecasting methods. Time-series forecasting, 2, 28–39. https://doi.org/10.1201/9781420036206.ch5
  36. [36] Iwok, I. A., & Okpe, A. S. (2016). A comparative study between univariate and multivariate linear stationary time series models. American journal of mathematics and statistics, 6(5), 203–212. https://doi.org/10.5923/j.ajms.20160605.02

Downloads

Published

2024-05-15

Issue

Vol. 1 No. 1 (2024): Trends in Health Informatics

Section

Articles

How to Cite

Kashyap, S., & Singh, A. (2024). Impact of statistical approach on time-series models for forecasting COVID-19. Trends in Health Informatics, 1(1), 9-22. https://doi.org/10.22105/thi.v1i1.18

Similar Articles

You may also start an advanced similarity search for this article.