Empirical mode decomposition (EMD) and BP_AdaBoost neural network are
used in this paper to model the oil price. Based on the benefits of these two
methods, we predict the oil price by using them. To a certain extent, it effectively
improves the accuracy of short-term price forecasting. Forecast results of this
model are compared with the results of the ARIMA model, BP neural network and
EMD-BP combined model. The experimental result shows that the root mean square
error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE)
and Theil inequality (U) of EMD and BP_AdaBoost model are lower than other
models, and the combined model has better prediction accuracy.
References
[1]
Torrence, C. and Compo, G.P. (1998) A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79, 61-78.
https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
[2]
Huang, N.E., Shen, Z., Long, S.R., et al. (1998) The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454, 903-995. https://doi.org/10.1098/rspa.1998.0193
[3]
Yu, L., Wang, S. and Lai, K.K. (2008) Forecasting Crude Oil Price with an EMD-Based Neural Network Ensemble Learning Paradigm. Energy Economics, 30, 2623-2635. https://doi.org/10.1016/j.eneco.2008.05.003
[4]
Zhang, Y.J. and Wei, Y.M. (2010) The Crude Oil Market and the Gold Market: Evidence for Cointegration, Causality and Price Discovery. Resources Policy, 35, 168-177. https://doi.org/10.1016/j.resourpol.2010.05.003
[5]
Islam, M.R., Rashedalmahfuz, M., Ahmad, S., et al. (2012) Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition. Discrete Dynamics in Nature and Society, 2012, 87-88.
https://doi.org/10.1155/2012/593018
[6]
Xiong, T., Bao, Y.K. and Hu, Z.Y. (2013) Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices. Energy Economics, 40, 405-415. https://doi.org/10.1016/j.eneco.2013.07.028
[7]
Wei, L.Y. (2016) A Hybrid ANFIS Model Based on Empirical Mode Decomposition for Stock Time Series Forecasting. Applied Soft Computing, 42, 368-376.
https://doi.org/10.1016/j.asoc.2016.01.027
[8]
Huang, N.E., Shen, Z. and Long, S.R. (1999) A New View of Nonlinear Water Waves—The Hilbert Spectrum. Annual Review of Fluid Mechanics, 31, 417–457.
https://doi.org/10.1146/annurev.fluid.31.1.417
[9]
Hu, D., Zheng, D. and Fu, H. (2015) Application of AdaBoost-BP Model to Dam Deformation Prediction. Journal of China Three Gorges University, 37, 5-8.
