多尺度视角下区间型金融时间序列组合预测模型

Combination Forecasting Model of Interval Financial Time Series from Multi-Scale Perspective

在“互联网+大数据”的背景下,搜索引擎为人类提供了多源的瞬时信息。在预测中,由于预测系统的复杂性,区间数作为刻画事物随机阶段性信息的一种表现形式,蕴含信息较时点序列更加丰富。而传统的区间组合预测模型并不能很好地处理非线性时间序列,因此,论文研究多尺度视角下区间组合预测模型及其在金融时间序列中的应用。首先利用改进的BEMD算法对区间金融时间序列进行多尺度分解,其次利用三种区间型单项预测方法对分解后的序列进行单项预测,最后组合单项预测的结果得到最优组合预测结果,通过对上证指数的实证,验证了论文所提多尺度区间组合预测模型的有效性。

Under the background of"internet+big data",search engines provide human with instantaneous information of multiple sources.In forecasting,because of the complexity of the forecasting system,interval number,as a form of expression to describe the random periodic information of things,contains more rich information than the time point series.However,the traditional interval combination forecasting model can not deal with the nonlinear time series well.Therefore,this paper studies the interval combination forecasting model from the multi-scale perspective and its application in financial time series.The paper firstly performs a multi-scale decomposition of interval financial time series using the improved BEMD algorithm,and then uses three interval single forecasting methods to perform single forecasting on the decomposed series,and finally combines the results of the single forecasting to obtain the optimal combination forecasting results.The validity of the multi-scale interval combination forecasting model proposed in the paper is verified through the empirical evidence of the Shanghai Composite Index.