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https://doi.org/10.1155/2012/593018

has been cited by the following article:

• TITLE: Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network

  AUTHORS: Huifang Qu, Guoqiang Tang, Qiying Lao

  KEYWORDS: Empirical Mode Decomposition (EMD), BP_AdaBoost Model, Oil Price

  JOURNAL NAME: Open Journal of Statistics, Vol.8 No.4, August 3, 2018

  ABSTRACT: 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.