基于误差修正与分解的区间值股价时间序列预测研究

Error correction and decomposition method for forecast of interval-valued stock price time series

与点值数据相比,区间数据更能够从全局上刻画股票市场的内在结构特征.然而,已有关于区间数据的预测研究只关注误差序列的单次预测或原始序列的预处理,并且所采用的方法通常不能充分地提取区间值股价时间序列的主要特征.因此,本文提出了对区间值股价时间序列进行预测的误差修正与分解方法.鉴于误差序列在组合预测模型中的作用,本文首先采用Ljung-Box检验和机器学习模型对原始序列产生的区间值误差序列进行检验和修正.接着,利用双变量经验模态分解技术将修正后的误差序列分解成多个本征模态函数(IMFs)和一个残差.随后,利用单个机器学习模型对除了IMF1分量外的各个IMFs分量和残差分别进行预测.最后,将原始序列和误差序列的预测值进行组件聚合,重构出区间值股票价格的预测值.进一步,在提出方法的基础上,本文构建了基于误差修正和分解的区间值股价预测模型,并利用真实的股票市场数据进行实证分析.实验结果表明,所提出的方法在预测精度方面明显优于一些传统方法.

Compared with point data,interval data can better grasp the internal structural characteristics of financial markets from a global perspective.However,the existing prediction research on interval data only focuses on the single prediction of error series or the preprocessing of original series,and the methods usually cannot fully extract the main features of interva stock price time series.Therefore,this paper proposes an error correction and decomposition method for forecast of interval-valued stock price time series.In view of the role of the error series in the combination forecasting model,we first use Ljung-Box test and machine learning model to test and modify the interval-valued error series generated by the original series.Then,bivariate empirical mode decomposition technique is used to decompose the corrected error series into multiple intrinsic mode functions(IMFs)and a residual.Then,a single machine learning model is used to predict each IMFs component and residual except IMF1 component.Finally,the predicted values of the original series and the error series are aggregated to reconstruct the predicted values of the interval stock price.Furthermore,on the basis of the proposed method,we build an interval-valued stock price forecasting model based on error correction and decomposition,and use real stock market data for empirical analysis.Experimental results show that the proposed method is superior to some traditional methods in prediction accuracy.