COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES
Abstract
The emergence of the Covid-19 outbreak for the first time in China killed thousands to millions of people. Since the beginning of the emergence of the number of cases of Covid-19 continues to increase until now. The increase in Covid-19 cases has a very bad impact on health, social and economic life. The need for future forecasting to predict the number of deaths and recoveries from cases that occur, so that the government and the public can understand the spread, prevent and plan actions as early as possible. Several previous studies have forecast the future impact of Covid-19 using the Machine Learning method. Time series forecasting can be done using traditional methods with Linear Regression or Artificial Intelligent methods with neural networks. In this study, it has been proven that there is a linear relationship in the time series data of Covid-19 recovered cases in China, so it is proven that the performance of Linear Regression is better than the Neural Network.
Downloads
References
Abdel-Aal, R. E., & Al-Garni, A. Z. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy, 22(11), 1059–1069. https://doi.org/10.1016/S0360-5442(97)00032-7
Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2019). Ensemble of relevance vector machines and boosted trees for electricity price forecasting. Applied Energy, 250(May), 540–548. https://doi.org/10.1016/j.apenergy.2019.05.062
Aiken, L. S., West, S. G., Pitts, S. C., Baraldi, A. N., & Wurpts, I. C. (2013). Multiple Linear Regression. In Handbook of Psychology. John Wiley & Sons, Inc. https://doi.org/10.1002/0471264385.wei0219
Brockmann, D., Hufnagel, L., & Geisel, T. (2006). Data Mining and Knowledge Discovery Handbook. In Springer. https://doi.org/10.1038/nature04292
Castillo, O., & Melin, P. (2020). Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos, Solitons and Fractals, 140, 110242. https://doi.org/10.1016/j.chaos.2020.110242
Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons and Fractals, 135. https://doi.org/10.1016/j.chaos.2020.109864
Choi, H., Ryu, S., & Kim, H. (2018). Short-Term Load Forecasting based on ResNet and LSTM. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018. https://doi.org/10.1109/SmartGridComm.2018.8587554
Dodamani, S. N., Shetty, V. J., & Magadum, R. B. (2015). Short term load forecast based on time series analysis: A case study. Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015, 299–303. https://doi.org/10.1109/TAPENERGY.2015.7229635
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons and Fractals, 134, 109761. https://doi.org/10.1016/j.chaos.2020.109761
Fang, T., & Lahdelma, R. (2016). Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system. Applied Energy, 179, 544–552. https://doi.org/10.1016/j.apenergy.2016.06.133
Ferreira, V. H., & Alves da Silva, A. P. (2007). Toward estimating autonomous neural network-based electric load forecasters. IEEE Transactions on Power Systems, 22(4), 1554–1562. https://doi.org/10.1109/TPWRS.2007.908438
Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing Journal, 93(December 2019), 106282. https://doi.org/10.1016/j.asoc.2020.106282
Fumo, N., & Rafe Biswas, M. A. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343. https://doi.org/10.1016/j.rser.2015.03.035
George, D., & Huerta, E. A. (2018). Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data. Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics, 778, 64–70. https://doi.org/10.1016/j.physletb.2017.12.053
Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/nejmoa2002032
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. In Data Mining. https://doi.org/10.1016/b978-0-12-381479-1.00001-0
Kavadi, D. P., Patan, R., Ramachandran, M., & Gandomi, A. H. (2020). Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons and Fractals, 139. https://doi.org/10.1016/j.chaos.2020.110056
Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036
Krätschmer, V. (2006). Strong consistency of least-squares estimation in linear regression models with vague concepts. Journal of Multivariate Analysis, 97(3), 633–654. https://doi.org/10.1016/j.jmva.2005.04.009
Livingston, E., Bucher, K., & Rekito, A. (2020). Coronavirus Disease 2019 and Influenza 2019-2020. In JAMA - Journal of the American Medical Association (Vol. 323, Issue 12, p. 1122). https://doi.org/10.1001/jama.2020.2633
Nadtoka, I. I., & Al-Zihery Balasim, M. (2015). Mathematical modelling and short-term forecasting of electricity consumption of the power system, with due account of air temperature and natural illumination, based on support vector machine and particle swarm. Procedia Engineering, 129, 657–663. https://doi.org/10.1016/j.proeng.2015.12.087
Peng, Y., & Nagata, M. H. (2020). An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons and Fractals, 139. https://doi.org/10.1016/j.chaos.2020.110055
Pombeiro, H., Santos, R., Carreira, P., Silva, C., & Sousa, J. M. C. (2017). Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks. Energy and Buildings, 146, 141–151. https://doi.org/10.1016/j.enbuild.2017.04.032
Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(5), 1467–1474. https://doi.org/10.1016/j.dsx.2020.07.045
Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & Coelho, L. dos S. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons and Fractals, 135, 1–10. https://doi.org/10.1016/j.chaos.2020.109853
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., Yan, P., & Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, 256–263. https://doi.org/10.1016/j.idm.2020.02.002
Satre-Meloy, A. (2019). Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models. Energy, 174, 148–168. https://doi.org/10.1016/j.energy.2019.01.157
Setiyorini, T., & Frieyadie, F. (2022). Laporan Akhir Penelitian Mandiri:
Shao, M., Wang, X., Bu, Z., Chen, X., & Wang, Y. (2020). Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society, 57(March), 102128. https://doi.org/10.1016/j.scs.2020.102128
Shim, E., Tariq, A., Choi, W., Lee, Y., & Chowell, G. (2020). Transmission potential and severity of COVID-19 in South Korea. International Journal of Infectious Diseases, 93, 339–344. https://doi.org/10.1016/j.ijid.2020.03.031
WHO. (2021). Coronavirus Disease 2019 (COVID 19): Situation Report - 53. Covid 19. https://cdn.who.int/media/docs/default-source/searo/indonesia/covid19/external-situation-report-53_28-april-2021.pdf?sfvrsn=c2563ad9_9
Yan, K., Li, W., Ji, Z., Qi, M., & Du, Y. (2019). A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households. IEEE Access, 7, 157633–157642. https://doi.org/10.1109/ACCESS.2019.2949065
Zhang, X., Ma, R., & Wang, L. (2020). Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos, Solitons and Fractals, 135. https://doi.org/10.1016/j.chaos.2020.109829
Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., & Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214–217. https://doi.org/10.1016/j.ijid.2020.01.050
Abstract viewed = 25 times
PDF downloaded = 15 times
Copyright (c) 2022 Tyas Setiyorini, Frieyadie Frieyadie
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Jurnal Riset Informatika agrees to the following terms:
- The author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author is permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
