EMD结合RBF神经网络新混合模型及股指期货价格预测

中国近4年才成立的股指期货市场价格呈现出非平稳、非线性的信号特征,传统的预测方法无法对长相关序列进行精确预测.将EMD与RBF相结合,建立了一种新的预测方法对我国股指期货日结算价格进行预测.结果显示本模型将原本具有长相关性质的原始序列分解为若干个短相关性质的不同频带,解决了原始序列随机性强,以及因相邻频带的干扰而造成的系统动力信息反映不足的缺陷;并与其他预测模型进行比较,显示出较高的预测精度.

Prediction of the Chinese Stock Index Futures Market Based on New EMD-RBF Model

Only in the past four years did China set up the stock index futures market displaying the non-stable and non-linear signal features. The traditional estimation methods cannot make accurate estimation of long-relevant sequence. Combining EMD with RBF, we have created a new method of estimation to predict the daily settlement price for stock index futures. The result shows that this model has separated the original sequence with long-relevance features into several short-relevance frequency bands, making up for the shortage of system power information caused by the serious randomness of the original sequence and the interruptions from nearby frequency bands. It is also compared with other estimation models to display a relatively high degree of accuracy.