CAJ | 학술논문

结合经验模态分解(empirical mode decomposition，EMD)算法和自适应神经模糊推理系统(adap-tive neural fuzzy inference system，ANFIS)算法应用于股票市场预测,提出了一种新的股票市场的预测模型,即EMD-ANFIS的多步预测模型。首先应用EMD算法把原始数据分解成不同尺度的基本模态函数(IMF)和残差(RES),然后通过ANFIS算法对生成的各个IMF和RES进行自适应神经模糊推理,再把各个预测结果进行简单的聚合作为股票的预测价格,并与传统的预测方法进行比较,实验证明了EMD-ANFIS的多步预测模型具有更高的预测精度。
결합경험모태분해(empirical mode decomposition，EMD)산법화자괄응신경모호추리계통(adap-tive neural fuzzy inference system，ANFIS)산법응용우고표시장예측,제출료일충신적고표시장적예측모형,즉EMD-ANFIS적다보예측모형。수선응용EMD산법파원시수거분해성불동척도적기본모태함수(IMF)화잔차(RES),연후통과ANFIS산법대생성적각개IMF화RES진행자괄응신경모호추리,재파각개예측결과진행간단적취합작위고표적예측개격,병여전통적예측방법진행비교,실험증명료EMD-ANFIS적다보예측모형구유경고적예측정도。
By Integrating Empirical Model Decomposition (EMD) with Adaptive Neural Fuzzy Inference System (ANFIS) theory, a new stock market forecasting model, namely EMD-ANFIS Multi-step Model is put forward. Firstly, EMD theory is employed to decompose original stock market time series into residual (RES) and Intrinsic Model Functions (IMF) of different scales. Then ANFIS theory is employed for the adaptive neuro-fuzzy inference processing of IMF and RES. Finally, by simple addition, the sum of the predicted results is considered as the predicted stock price. Compared with traditional forecasting methods, this experiment proves that EMD-ANFIS method can effectively improve the prediction accuracy.