A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting
A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting
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DOI：
10.1016/j.asoc.2016.01.027
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发表时间：
2016-05
期刊：
影响因子：
--
通讯作者：
Liang-Ying Wei
中科院分区：
文献类型：
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作者：
Liang-Ying Wei
时间序列预测是系统建模研究中的一个重要课题，而股指预测是时间序列预测中的一个重要问题。准确的股价预测是金融时间序列预测中的一项具有挑战性的任务。时间序列方法已经成功地应用于包括股票市场在内的许多领域的预测模型。遗憾的是，使用时间序列方法研究股票市场有三大缺陷：(1)一些模型不能应用于不符合统计假设的数据集；(2)大多数时间序列模型使用带有大量噪声的股票数据(由市场状况和环境的变化引起)预测性能较差；为了解决这些问题，提高时间序列模型的预测性能，提出了一种以经验模式分解(EMD)为核心的混合时间序列自适应网络模糊推理系统(ANFIS)模型，用于预测台股加权指数(TAIEX)和恒生指数(HSI)的股价。为了衡量其预测性能，将该模型与Chen模型、Yu模型、自回归(AR)模型、ANFIS模型和支持向量回归(SVR)模型进行了比较。结果表明，基于均方根误差(RMSE)，我们的模型优于其他模型。
Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.