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A hybrid model for financial time‐series forecasting based on mixed methodologies
Expert Systems ( IF 3.3 ) Pub Date : 2020-09-02 , DOI: 10.1111/exsy.12633
Zhidan Luo 1 , Wei Guo 1 , Qingfu Liu 2 , Zhengjun Zhang 3
Affiliation  

This paper proposes a hybrid model that combines ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and Taylor expansion using a tracking differentiator to forecast financial time series. Specifically, the financial time series is decomposed by EEMD into some subseries. Then, the linear portion of each subseries is forecasted by the linear ARIMA model, while the nonlinear portion is predicted by the nonlinear Taylor expansion model. The forecasting results of the linear and nonlinear models are combined into the predicted result of each subseries. The final prediction result is obtained by combining the prediction values of all the subseries. The empirical results with real financial time series data demonstrate that this new hybrid approach outperforms the benchmark hybrid models considered in this paper.

中文翻译:

基于混合方法的金融时间序列预测混合模型

本文提出了一种混合模型,该模型结合了集成经验模式分解(EEMD),自回归综合移动平均线(ARIMA)和泰勒展开式,并使用跟踪微分器来预测金融时间序列。具体而言,EEMD将财务时间序列分解为一些子序列。然后,通过线性ARIMA模型预测每个子系列的线性部分,而通过非线性泰勒展开模型预测非线性部分。线性和非线性模型的预测结果被组合到每个子系列的预测结果中。通过组合所有子系列的预测值获得最终预测结果。
更新日期:2020-09-02
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