Hybrid approach of an empirical mode decomposition and wavelet support vector machine for forecasting Singapore tourist arrivals to Malaysia
Keywords:
Empirical mode decomposition (EMD), support vector machine (SVM), Wavelet support vector machine (WSVM)Abstract
Time series modelling and forecasting has fundamental importance to various practical domains. Thus, a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and efficiency of time series modelling and forecasting. This study presents a hybrid model empirical mode decomposition (EMD), wavelet and support vector machine (SVM) for Singapore tourist arrival to Malaysia. The EMD method is employed to decompose the monthly data tourist arrival to several intrinsic mode functions (IMFs) and residual. Wavelet support vector machine (WSVM) combines the advantages of wavelet analysis and SVM and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of EMD model and WSVM model. At last, the EMD_WSVM model is used to forecast monthly data of tourist arrivals from Singapore to Malaysia and the results show that the proposed combination model has better performance on forecasting accuracy compared with the other models.
References
Wang, H. and Hu, D. (2005). Comparison of SVM and LS-SVM for Regression, IEEE International Conference Neural Networks and Brain; p. 279–83.
Kim, G. and Yun, R.S. (2012). A Hybrid Forecast of Exchange Rate based on Discrete Grey-Markov and Grey Neural Network Model, preprint arXiv: 120.2254, Korea
Tang, B., Dong, S., Song, T., (2012). Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation. Signal Processing 92 (1), 248–258
Chiun-S, L. , Sheng-H, C. and Tzu-Y, L. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting, Volume 29, Issue 6, Pages 2583-2590
Peng, L. Fan, G., Huang, M. and Hong, W. (2016). Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting, Energies, 9, 221; doi: 10.3390/en9030221
Cheng, C.H. and Wei, L.Y. (2014). A novel timeseries model based on empirical mode decomposition for forecasting TAIEX. Economic Modelling, 36:136-141. [doi:10.1016/j.econmod.2013.09.033]
X. Xu, Y. Qi, & Z. Hua, Forecasting demand of commodities after natural disasters, Expert Systems with Applications, 37 (2010), 4313-4317
Zhang, P. G. (2003). Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, vol. 50, pp. 159–175
Ani, S. and Suhartono (2012). Streamflow forecasting using least squares support vector machines, Hydrological Sciences Journal: vol. 57, no. 7, pp. 1275–1293
Adamowski, J. and 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, vol. 15, no. 10, pp. 729–743
Gupta, U., & Solanki, H. (2014). Boron: Essential Micronutrient for Plant and Animal Nutrition. International Journal of Pharmacy Research and Technology (Vol. 4, pp. 12–21).
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