Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia

I Made Artha Agastya - Universitas Amikom Yogyakarta, Jalan Padjajaran, Sleman, 55283, Indonesia
Afrig Aminuddin - Universitas Amikom Yogyakarta, Jalan Padjajaran, Sleman, 55283, Indonesia

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Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future. 



covid-19; parametric; nonparametric; machine learning; autoregression; smoothing

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A. A. Al-Qahtani, "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Emergence, history, basic and clinical aspects," Saudi J. Biol. Sci., vol. 27, no. 10, pp. 2531–2538, 2020, doi: 10.1016/j.sjbs.2020.04.033.

Y.-R. Guo et al., "The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status," Mil. Med. Res., vol. 7, no. 1, p. 11, Dec. 2020, doi: 10.1186/s40779-020-00240-0.

V. J. Jayaraj, S. Rampal, C. W. Ng, and D. W. Q. Chong, "The Epidemiology of COVID-19 in Malaysia," Lancet Reg. Heal. - West. Pacific, vol. 17, p. 100295, 2021, doi: 10.1016/j.lanwpc.2021.100295.

J. M. Puaschunder, "The Potential for Artificial Intelligence in Healthcare," SSRN Electron. J., vol. 6, no. 2, pp. 94–98, 2020, doi: 10.2139/ssrn.3525037.

İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons and Fractals, vol. 138, 2020, doi: 10.1016/j.chaos.2020.110015.

R. Sujath, J. M. Chatterjee, and A. E. Hassanien, "A machine learning forecasting model for COVID-19 pandemic in India," Stoch. Environ. Res. Risk Assess., vol. 34, no. 7, pp. 959–972, 2020, doi: 10.1007/s00477-020-01827-8.

N. Alballa and I. Al-Turaiki, "Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review," Informatics Med. Unlocked, vol. 24, p. 100564, 2021, doi: 10.1016/j.imu.2021.100564.

H. Zakiyyah and S. Suyanto, "Prediction of Covid-19 Infection in Indonesia Using Machine Learning Methods," J. Phys. Conf. Ser., vol. 1844, no. 1, pp. 1–6, 2021, doi: 10.1088/1742-6596/1844/1/012002.

Y. Suzuki, A. Suzuki, S. Nakamura, T. Ishikawa, and A. Kinoshita, "Machine learning model estimating number of COVID-19 infection cases over coming 24 days in every province of South Korea (XGBoost and MultiOutputRegressor)," medRxiv, 2020.

E. Gothai, R. Thamilselvan, R. R. Rajalaxmi, R. M. Sadana, A. Ragavi, and R. Sakthivel, "Prediction of COVID-19 growth and trend using machine learning approach," Mater. Today Proc., 2021.

M. Amiruzzaman, M. Abdullah-Al-wadud, R. M. Nor, and N. A. Aziz, "Evaluation of the effectiveness of movement control order to limit the spread of COVID-19," Ann. Emerg. Technol. Comput., vol. 4, no. 4, pp. 1–9, 2020, doi: 10.33166/AETiC.2020.04.001.

A. H. Elsheikh, A. I. Saba, H. Panchal, S. Shanmugan, N. A. Alsaleh, and M. Ahmadein, "Artificial intelligence for forecasting the prevalence of covid-19 pandemic: An overview," Healthc., vol. 9, no. 12, pp. 1–20, 2021, doi: 10.3390/healthcare9121614.

I. Rahimi, F. Chen, and A. H. Gandomi, "A review on COVID-19 forecasting models," Neural Comput. Appl., vol. 8, 2021, doi: 10.1007/s00521-020-05626-8.

W. S. Putra, A. Aminuddin, I. H. Purwanto, R. S. Kurnia, and I. A. Astuti, “Estimation of Transmission Rate and Recovery Rate of SIR Pandemic Model Using Kalman Filter,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 12, pp. 323–328, 2022, doi: 10.14569/IJACSA.2022.0131239.

V. Geetha Mohan, A.-F. F. Mubarak Ali, M. Ariff Ameedeen, B. Lathakumary Vijayan, A. Aminuddin, and W. Widayani, "Predictive Models Using Supervised Neural Network for Pollutant Removal Efficiency in Petrochemical Wastewater Treatment," 2022 5th Int. Conf. Inf. Commun. Technol., pp. 116–121, Aug. 2022, doi: 10.1109/ICOIACT55506.2022.9971929.

Q. Waseem, W. Isni Sofiah Wan Din, A. Aminuddin, M. Hussain Mohammed, and R. F. Alfa Aziza, "Software-Defined Networking (SDN): A Review," 2022 5th Int. Conf. Inf. Commun. Technol., pp. 30–35, Aug. 2022, doi: 10.1109/ICOIACT55506.2022.9972067.

A. Cucus, L. B. Aji, A.-F. F. Bin Mubarak Ali, A. Aminuddin, and L. D. Farida, "Selection of Prospective Workers Using Profile Matching Algorithm on Crowdsourcing Platform," 2022 5th Int. Conf. Inf. Commun. Technol., pp. 122–126, Aug. 2022, doi: 10.1109/ICOIACT55506.2022.9972155.

A. Gautam and V. Singh, "Parametric versus non-parametric time series forecasting methods: A review," J. Eng. Sci. Technol. Rev., vol. 13, no. 3, pp. 165–171, 2020, doi: 10.25103/JESTR.133.18.

R. Khaldi, A. El Afia, and R. Chiheb, "Forecasting of BTC volatility: comparative study between parametric and non-parametric models," Prog. Artif. Intell., vol. 8, no. 4, pp. 511–523, 2019, doi: 10.1007/s13748-019-00196-w.

T. Kocsis, I. Kovács-Székely, and A. Anda, "Comparison of parametric and non-parametric time-series analysis methods on a long-term meteorological data set," Cent. Eur. Geol., vol. 60, no. 3, pp. 316–332, 2017, doi: 10.1556/24.60.2017.011.

A. Mahmud and P. Y. Lim, "Applying the SEIR Model in Forecasting The COVID-19 Trend in Malaysia: A Preliminary Study," medRxiv, p. 2020.04.14.20065607, 2020.

N. Salim et al., "COVID-19 epidemic in Malaysia: Impact of lockdown on infection dynamics," medRxiv, no. May, pp. 1–27, 2020.

A. S. S. Zamri et al., "Effectiveness of the movement control measures during the third wave of COVID-19 in Malaysia," Epidemiol. Health, vol. 43, pp. 1–8, 2021, doi: 10.4178/epih.e2021073.

A. Abidemi, Z. M. Zainuddin, and N. A. B. Aziz, Impact of control interventions on COVID-19 population dynamics in Malaysia: a mathematical study, vol. 136, no. 2. Springer Berlin Heidelberg, 2021.

K. M. U. B. Konarasinghe, "Forecasting COVID-19 Spread in Malaysia, Thailand, and Singapore," J. New Front. Healthc. Biol. Sci., vol. 1, no. 2, pp. 1–13, 2020.

H. A. Abd Rahman, M. A. Rahman, A. N. Rozaimi, and I. B. Zulnahar, "Forecasting of COVID-19 in Malaysia: Comparison of Models," 2021 IEEE Int. Conf. Comput. ICOCO 2021, pp. 324–329, 2021, doi: 10.1109/ICOCO53166.2021.9673498.

S. Singh et al., "Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models," J. Infect. Dev. Ctries., vol. 14, no. 9, pp. 971–976, 2020, doi: 10.3855/JIDC.13116.

M. A. Edre, M. A. ZA, and A. R. Jamalludin, "Forecasting Malaysia COVID-19 incidence based on movement control order using ARIMA and expert modeler," IIUM Med. J. Malaysia, vol. 19, no. 2, 2020.

C. V. Tan et al., "Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia," Int. J. Environ. Res. Public Health, vol. 19, no. 3, 2022, doi: 10.3390/ijerph19031504.

A. Alsayed, H. Sadir, R. Kamil, and H. Sari, "Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020," Int. J. Environ. Res. Public Health, vol. 17, no. 11, pp. 1–15, 2020, doi: 10.3390/ijerph17114076.

S. P. NYONI, T. NYONI, and T. A. CHIHOHO, "Forecasting Covid-19 New Cases in Malaysia," Int. Res. J. Innov. Eng. Technol., vol. 5, no. 6, pp. 316–321, 2021, doi: Forecasting.

W. M. A. W. Ahmad et al., "Forecasting cumulative covid-19 cases in malaysia and rising to unprecedented levels," Bangladesh J. Med. Sci., vol. 20, no. 3, pp. 504–510, 2021, doi: 10.3329/bjms.v20i3.52791.

T. Purwandari, S. Zahroh, Y. Hidayat, Sukono, M. Mamat, and J. Saputra, "Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network," Decis. Sci. Lett., vol. 11, no. 1, pp. 35–42, 2022, doi: 10.5267/j.dsl.2021.10.001.

N. M. Norwawi, Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia. Elsevier Inc., 2021.

A. A. A. Othman, Nurul ashikin Binti Aziz, N. A. Ahmad, M. H. Mohd, and S. I. M. Adam, "Analysing Trends and Forecasting of COVID-19 Pandemic in Malaysia using Singular Spectrum Analysis," Mat. Malaysian J. Ind. Appl. Math., pp. 121–134, 2021.

S. M. Shaharudin, S. Ismail, N. A. Hassan, M. L. Tan, and N. A. F. Sulaiman, "Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model," Front. Public Heal., vol. 9, no. June, pp. 1–14, 2021, doi: 10.3389/fpubh.2021.604093.

H. Hasri, S. A. M. Aris, and R. Ahmad, "Linear Regression and Holt's Winter Algorithm in Forecasting Daily Coronavirus Disease 2019 Cases in Malaysia: Preliminary Study," 1st Natl. Biomed. Eng. Conf. NBEC 2021, pp. 157–160, 2021, doi: 10.1109/NBEC53282.2021.9618763.

A. N. A. Kamarudin, Z. Zainol, N. F. A. Kassim, and R. Sharif, "Prediction of COVID-19 cases in Malaysia by using machine learning: A preliminary testing," 2021 Int. Conf. Women Data Sci. Taif Univ. WiDSTaif 2021, 2021, doi: 10.1109/WIDSTAIF52235.2021.9430222.