UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving averageleast-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving averagesupport vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving averagesupport vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving averagesupport vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving averagesupport vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency. 2023 by the authors. |
References |
Abdullah, M.T.; Lola, M.S.; Hisham, A.E.; Sabreena, S.; Nor Fazila, C.M.; Idham, K.; Dennis, C.Y.T. Framework of Measures for COVID-19 Pandemic in Malaysia: Threats, Initiatives and Opportunities. J. Sustain. Sci. Manag. 2022, 17, 8–18. [CrossRef] Ali, M.; Khan, D.M.; Aamir, M.; Khalil, U.; Khan, Z. Forecasting COVID-19 in Pakistan. PLoS ONE 2020, 15, e0242762. [CrossRef] [PubMed] WHO. Coronavirus Disease (COVID-19) in Malaysia. 2020. Available online: https://www.who.int/malaysia/emergencies/coronavirus-disease-(COVID-19)-in-Malaysia (accessed on 23 May 2020). KKM. COVID-19 Malaysia: Situasi Terkini 25 Oktober 2020. 2020. Available online: https://covid-19.moh.gov.my/terkini (accessed on 25 June 2022). Gecili, E.; Ziady, A.; Szczesniak, R.D. Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy. PLoS ONE 2021, 16, e0244173. [CrossRef] Awwad, F.A.; Mohamoud, M.A.; Abonazel, M.R. Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling. PLoS ONE 2021, 16, e0250149. [CrossRef] Sahai, A.K.; Rath, N.; Sood, V.; Singh, M.P. ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 1419–1427. [CrossRef] Alzahrani, S.I.; Aljamaan, I.A.; Al-Fakih, E.A. Forecasting the Spread Of The COVID-19 Pandemic In Saudi Arabia Using ARIMA Prediction Model Under Current Public Health Interventions. J. Infect. Public Health. 2020, 13, 914–919. [CrossRef] Benvenuto, D.; Giovanetti, M.; Vassallo, L.; Angeletti, S.; Ciccozzi, M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief 2020, 29, 105340. [CrossRef] Ceylan, Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci. Total Environ. 2020, 729, 138817. [CrossRef] Hernandez-Matamoros, A.; Fujita, H.; Hayashi, T.; Perez-Meana, H. Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Appl. Soft Comput. 2020, 96, 106610. [CrossRef] Khan, F.M.; Gupta, R. ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. J. Saf. Sci. Resil. 2020, 1, 12–18. [CrossRef] Kayode, O.; Fahimah, A.; Mustapha, R.; Jacques, D. Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics. Kuwait J. Sci. Special Issue 2021, 1–30. [CrossRef] Rahman, M.S.; Chowdhury, A.H.; Amrin, M. Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh. PLoS Glob. Public Health 2022, 2, e0000495. [CrossRef] Aisyah, W.I.W.M.N.; Muhamad Safiih, L.; Razak, Z.; Nurul Hila, Z.; Abd Aziz, K.A.H.; Elayaraja, A.; Nor Shairah, A.Z. Improved of Forecasting Sea Surface Temperature based on Hybrid ARIMA and Vector Machines Model. Malays. J. Fundam. Appl. Sci. 2021, 17, 609–620. [CrossRef] Nurul Hila, Z.; Muhamad Safiih, L.; Maman Abdurachman, D.; Fadhilah, Y.; Mohd Noor Afiq, R.; Aziz, D.; Yahaya, I.; Mohd Tajuddin, A. Improvement of Time Forecasting Models using A Novel Hybridization of Bootstrap and Double Bootstrap Artificial Neural Networks. Appl. Soft Comput. J. 2019, 84, 105676. [CrossRef] Lee, M.C. Using support vector machine with a hybrid feature selection method to the stock trend prediction. J. Expert Syst. Appl. 2009, 36, 10896–10904. [CrossRef] Vapnik, V.N. The Nature of Statistical Learning Theory, 1st ed.; Springer: New York, NY, USA, 1995. 19. Sudheer, C.; Maheswaran, R.; Panigrahi, B.K.; Mathur, S. A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput. Appl. 2013, 24, 1381–1389. [CrossRef] Chakraborty, T.; Chakraborty, A.K.; Biswas, M.; Banerjee, S.; Bhattacharya, S. Unemployment Rate Forecasting: A Hybrid Approach. Comput. Econ. 2020, 57, 183–201. [CrossRef] Zhang, G.P. Time series forecasting using a hybrid ARIMA and Neural Network Model. Neurocomputing 2003, 50, 159–175. [CrossRef] Terui, N.; Van Dijk, H. Combined forecasts from linear and nonlinear time series models. Int. J. Forecast. 2002, 18, 421–438. [CrossRef] Wang, X.; Meng, M. A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting. J. Comput. 2012, 7, 1184–1190. [CrossRef] Muhamad Safiih, L.; Nurul Hila, Z.; Mohd Tajuddin, A.; Vigneswary, P.; Mohd Noor Afiq, R.; Razak, Z.; Suffian, I.; Khalili, I. Improving the Performance of ANN-ARIMA Models for PredictingWater Quality in The Offshore Area of Kuala Terengganu, Terengganu, Malaysia. J. Sustain. Sci. Manag. 2018, 13, 27–37. Pai, P.F.; Lin, C.-S. A hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting. Int. J. Manag. Sci. 2005, 3, 497–505. [CrossRef] Lee, N.-U.; Shim, J.-S.; Ju, Y.-W.; Park, S.-C. Design and Implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy. Soft Comput. 2017, 22, 4275–4281. [CrossRef] Hao, Y.; Xu, T.; Hu, H.; Wang, P.; Bai, Y. Prediction and analysis of Corona Virus Disease 2019. PLoS ONE 2020, 15, e0239960. [CrossRef] [PubMed] Roy, S.; Ghosh, P. Factors affecting COVID-19 infected and death rates inform lockdown- related policymaking. PLoS ONE 2020, 15, e0241165. [CrossRef] [PubMed] Mahdavi, M.; Choubdar, H.; Zabeh, E.; Rieder, M.; Safavi-Naeini, S.; Jobbagy, Z.; Ghorbani, A.; Abedini, A.; Kiani, A.; Khanlarzadeh, V.; et al. A machine learning based exploration of COVID-19 mortality risk. PLoS ONE 2021, 16, e0252384. [CrossRef] Singhal, T. A Review of Coronavirus Disease-2019 (COVID-19). Indian J. Pediatr. 2020, 87, 281–286. [CrossRef] 31. Qu, Z.; Li, Y.; Jiang, X.; Niu, C. An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting. Expert Syst. Appl. 2023, 212, 118746. [CrossRef] Zivkovic, M.; Bacanin, N.; KVenkatachalam Nayyar, A.; Djordjevic, A.; Strumberger, I.; Al-Turjman, F. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc. 2021, 66, 102669. [CrossRef] Melin, P.; Sánchez, D.; Castro, J.R.; Castillo, O. Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm. Axioms 2022, 11, 410. [CrossRef] Sarah, M. The Future of Pandemics. News-Medical. Available online: https://www.news-medical.net/health/The-Future-of-Pandemics.aspx (accessed on 17 January 2022). Suykens, J.A.K.; Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Process. Lett. 1999, 9, 293–300. [CrossRef] |
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