摘要
本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。
In this paper,an adaptive model is documented for predicting financial time series integrating signal processing,information fusion and computational intelligence.The model consists of financial time series（FTS）-specific Empirical Mode Decomposition（EMD）for signal processing,Principal Component Analysis（PCA）for dimension reduction,and Artificial Neural Networks（ANN）for nonlinear prediction.The model uses a sliding window to capture the most recent time series data,applies EMD to transform the data into multilevel Intrinsic Mode Functions（IMF＇s）.PCA is then used to reduce the dimension of IMF＇s and to generate a set of information-rich features which are input into an ANN to generate the output as prediction.This novel model of prediction implements an information fusion process consisting of signal decomposition,dimension reduction and nonlinear synthesis.This model lifts the prediction capability to a new level.The originality of this work exhibits in fouraspects：1）a structural reformulation of EMD algorithm,providing an interface to more information fusion,2）deepening into finer time frames for tackling the end effect of EMD andimplementation and testing on two levels of time frameimplementation,3）investigation of multi-step prediction,4）ageneric framework of prediction models for financial time series with upgradability and extensibility.The use of sliding window for EMD also gets the test closer to the reality.The new model is tested on the historical data of two stock indices– Chinese HS300 and Australian AORD,the performance,achieving a hit rate of 78%and 82% on HS300 D1 and M15,and 74% on AXJO D1 respectively,significantly higher than 5 existing models after comparison.
作者
潘和平
张承钊
PAN He-ping;ZHANG Cheng-zhao(Chongqing Institute of Finance,Chongqing 400067, China;School of Economics and Management, University of Electronic Science and Technology,Chengdu 611731, China;Chengdu Polytechnic,Chengdu 610041, China;Swingtum Prediction,Australia)
出处
《中国管理科学》
CSSCI
CSCD
北大核心
2018年第6期26-38,共13页
Chinese Journal of Management Science
基金
国家社科基金资助项目(17BGL231)
中国智能金融研究院(香港)资助项目
关键词
金融时间序列预测
经验模态分解
主成分分析
人工神经网络
本征模态函数
信息融合
预测可靠性
financial time series prediction
Empirical Mode Decomposition （EMD）
Intrinsic Mode Function （IMF）
Principal Component Analysis （PCA）
Artificial Neural Networks （ANN）
