A Comparative Simulation Study of Nonlinear Time Series Model for Forecasting Tourism Data
محورهای موضوعی : Design of Experiment
1 - Department of Technical Foundation, Universiti Kuala Lumpur (UniKL), Malaysian Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam, 81750, Johor Bahru, Malaysia
کلید واژه: EMD, SVM, EMDWSVM, WSVM,
چکیده مقاله :
Simulation is a tool to evaluate the performance existing and proposed under configure conditions of the simulation data. A simulation process can be useful to test theories and understand behavior of the statistical methods. This study aimed to compare SVM, WSVM and EMDWSVM model in order to identify the best model for forecasting time series data based on 10 replicates on 2040 generated data of the SARIMA (3,1,3) (3,1,1) [12] model of Brunei data set. This SARIMA model come from the lowest error between SARIMA models. The simulations were performed with three criteria namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results of the study show a lowest error value for the EMDWSVM time series model and the performance of all measurements is small then other models. The results also proved that combination of three method EMDWSVM is the advanced forecasting techniques in all the considered situation in providing better forecasting accuracy, the application of an EMD-based combined model particularly with wavelet method reduction approach for tourist arrivals forecasting due to better prediction results and stability than those achieved from single and current hybrid models. Therefore, the modified the existing hybrid model WSVM combined with the empirical mode decomposition (EMD) to decrease the complexity of dataset to improve its prediction accuracy.
Simulation is a tool to evaluate the performance existing and proposed under configure conditions of the simulation data. A simulation process can be useful to test theories and understand behavior of the statistical methods. This study aimed to compare SVM, WSVM and EMDWSVM model in order to identify the best model for forecasting time series data based on 10 replicates on 2040 generated data of the SARIMA (3,1,3) (3,1,1) [12] model of Brunei data set. This SARIMA model come from the lowest error between SARIMA models. The simulations were performed with three criteria namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results of the study show a lowest error value for the EMDWSVM time series model and the performance of all measurements is small then other models. The results also proved that combination of three method EMDWSVM is the advanced forecasting techniques in all the considered situation in providing better forecasting accuracy, the application of an EMD-based combined model particularly with wavelet method reduction approach for tourist arrivals forecasting due to better prediction results and stability than those achieved from single and current hybrid models. Therefore, the modified the existing hybrid model WSVM combined with the empirical mode decomposition (EMD) to decrease the complexity of dataset to improve its prediction accuracy.
Adamowski, J., & Karapataki, C. (2010). Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. Journal of Hydrologic Engineering, 15(10), 729-743.
Aamir, M., & Shabri, A. (2018). Improving crude oil price forecasting accuracy via decomposition and ensemble model by reconstructing the stochastic and deterministic influences. Advanced Science Letters, 24(6), 4337-4342.
Aizerman, M. A. (1964). Theoretical foundations of the potential function method in pattern recognition learning. Automation and remote control, 25, 821-837.
Altiok, T., & Melamed, B. (2010). Simulation modeling and analysis with Arena. Elsevier.
Baldigara, T. (2013). Forecasting Tourism Demand in Croatia: A Comparison of Different Extrapolative Methods. Journal of Business Administration Research, 2(1), 84.
Brida, J. G., & Garrido, N. (2011). Tourism forecasting using SARIMA models in Chilean regions. International Journal of Leisure and Tourism Marketing, 2(2), 176-190.
Chinnakum, W., & Boonyasana, P. (2016). Forecasting International Tourism Demand in Thailand. Thai Journal of Mathematics, 231-244.
Croce, V. (2018), “With growth comes accountability: could a leisure activity turn into a driver for sustainable growth?”, Journal of Tourism Futures, Vol. 4 No. 3, pp. 218-32.
Carson, J. S. (2005, December). Introduction to modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005. (pp. 8-pp). IEEE.
Chen, C. F., Lai, M. C., & Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281-287.
Duchêne, F., Garbay, C., & Rialle, V. (2003, October). An hybrid knowledge-based methodology for multivariate simulation in home health telecare. In Proc. of the Joint Workshop Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP) of the 9th Artificial Intelligence in Medicine Europe conference (AIME) (pp. 87-94).
Gustavsson, P., & Nordström, J. (2001). The impact of seasonal unit roots and vector ARMA modelling on forecasting monthly tourism flows. Tourism Economics, 7(2), 117-133.
Ghalehkhondabi, I., Ardjmand, E., Young, W. A., & Weckman, G. R. (2019). A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of Tourism Futures, 5(1), 75-93.
Haji, H. A., Sadik, K., & Soleh, A. M. (2018). A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
Jensen, A., & la Cour-Harbo, A. (2001). Ripples in mathematics: the discrete wavelet transform. Springer Science & Business Media.
Kulendran, N., & Witt, S. F. (2001). Cointegration versus least squares regression. Annals of Tourism Research, 28(2), 291-311.
Liang, Y. H. (2014). Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan. Computers & Industrial Engineering, 74, 111-119.
Loeb, P. D. (1982). International travel to the United States: an econometric evaluation. Annals of Tourism Research, 9(1), 7-20.
Lim, C., & McAleer, M. (2000). A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics, 32(4), 499-509.
Louvieris, P. (2002). Forecasting international tourism demand for Greece: A contingency approach. Journal of Travel & Tourism Marketing, 13(1-2), 21-40.
Liang, Y. H. (2016, July). Using the combined model for forecasting the tourism demand. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 612-615). IEEE.
Loganathan, N., & Ibrahim, Y. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins Sarima application. South Asian Journal of Tourism and Heritage, 3(2), 50-60.
Loganathan, N., Han, A. S., & Kogid, M. (2013). Demand for Indonesia, Singapore and Thailand tourist to Malaysia: seasonal unit root and multivariate analysis. International Journal of Economics and Empirical Research (IJEER), 1(2), 15-23.
Lingyu, T., Jun, W., & Chunyu, Z. (2021). Mode decomposition method integrating mode reconstruction, feature extraction, and ELM for tourist arrival forecasting. Chaos, Solitons & Fractals, 143, 110423.
Morris, T. P., White, I. R., & Crowther, M. J. (2019).
Using simulation studies to evaluate statistical methods. Statistics in medicine, 38(11), 2074-2102.
Norrulashikin, S. M., Yusof, F., & Kane, I. L. (2018). Performance Evaluation of a New Hybrid Multivariate Meteorological Model Analysis: A Simulation Study. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 73-85.
Nor, M. E., Nurul, A. I., & Rusiman, M. S. (2018, April). A hybrid approach on tourism demand forecasting. In Journal of Physics: Conference Series (Vol. 995, No. 1, p. 012034). IOP Publishing.
Nguyen, L. Q., Fernandes, P. O., & Teixeira, J. P. (2022). Analyzing and forecasting tourism demand in Vietnam with artificial neural networks. Forecasting, 4(1), 36-50.
Napolitano, G., Serinaldi, F., & See, L. (2011). Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination. Journal of Hydrology, 406(3-4), 199-214.
Rajaee, T., Nourani, V., Zounemat-Kermani, M., & Kisi, (2011). River suspended sediment load prediction: application of ANN and wavelet conjunction model. Journal of Hydrologic Engineering, 16(8), 613-627.
Robert Davies, Tim Coole, D. O. (2014) ‘The Application of Time Series Modelling and Monte Carlo Simulation: Forecasting Volatile Inventory Requirements’, Applied Mathematics, i(May), pp. 1152–1168.
Robinson, S. (2005). Distributed simulation and simulation practice. Simulation, 81(1), 5-13.
Shabri, A., & Suhartono. (2012). Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57(7), 1275-1293.
Shu, M. H., Hung, W. J., Nguyen, T. L., Hsu, B. M., & Lu, C. H. U. N. W. E. I. (2014). Forecasting with Fourier residual modified ARIMA model-An empirical case of inbound tourism demand in New Zealand. WSEAS Transactions on Mathematics, 13(1), 12-21.
Saayman, A., & Botha, I. (2017). Non-linear models for tourism demand forecasting. Tourism Economics, 23(3), 594-613.
Song, H., & Li, G. (2008). Tourism demand modelling
and forecasting—A review of recent research. Tourism management, 29(2), 203-220. Song, H., Li, G., Witt, S. F., & Athanasopoulos, G.
(2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27(3), 855-869.
Vapnik, V., Golowich, S. E., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, 281-287.
Wang, W., & Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1), 67-71.
Werthner, H., & Ricci, F. (2004). E-commerce and tourism. Communications of the ACM, 47(12), 101-105.
Wu, Q. (2010). Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system. Journal of computational and applied mathematics, 233(10), 2481-2491.
Wong, K. K. F., & Song, H. (2012). Tourism forecasting and marketing. Psychology Press.
Wang, H., & Liu, W. (2022). Forecasting Tourism Demand by a Novel Multi-Factors Fusion Approach. IEEE Access, 10, 125972-125991.
Yu, S. P., Yang, J. S., & Liu, G. M. (2013). A novel discussion on two long-term forecast mechanisms for hydro-meteorological signals using hybrid wavelet-NN model. Journal of hydrology, 497, 189-197.
Zhou, D., Yanagida, J. F., Chakravorty, U., & Leung, P. (1997). Estimating economic impacts from tourism. Annals of Tourism Research, 24(1), 76-89.