A Model of Oil Price Forecasting based on Autoregressive and Moving Average

被引：4
作者：
Mo, Zhou [1 ]
Tao, Han [1 ]
机构：
[1] PetroChina Int Iraq FZE, Beijing 100011, Peoples R China
来源：
2016 INTERNATIONAL CONFERENCE ON ROBOTS & INTELLIGENT SYSTEM (ICRIS) | 2016年
关键词：
Oil Price Forecasting; Autoregressive and Moving Average Model; accuracy; SWARM OPTIMIZATION ALGORITHM; PREDICTION; NETWORK; SOLUBILITY; POLYMERS;
D O I：
10.1109/ICRIS.2016.4
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents the basic principle and application of ARIMA model, and this model is used to analyze crude oil price time series and obtain the second order linear regression model. The model is used to predict the price of crude oil. On this basis, according to the residual error after fitting the existing nonlinear ARIMA model, the fitting residual error is analyzed by using the artificial neural network. Finally, through the combination of ARIMA and RBF neural network, the prediction of crude oil price series outside of the sample. The experimental results show that the combined model is better.
引用
收藏
页码：22 / 25
页数：4
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