Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network

• 作者: Guoqiang Tang Huifang Qu Qiying Lao
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Empirical Mode DECOMPOSITION (EMD) BP_AdaBoost Model Oil PRICE
• 年，卷（期）: 2018,（4）
• 页码范围: 660-669
• 页数: 10页
• 分类号: R73

摘要

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.