Three-way Clustering Model Integrated Decomposition Ensemble Learning for Forecasting Stock Price
Accurate trend analysis and real-time price prediction are effective ways to achieve optimal investment returns.However,traditional forecasting methods face challenges in the financial markets,which are influenced by changes in the objective economic environment,investors'expected returns,and other underlying factors.How to discover a reliable forecasting tool in uncertain environments and improve prediction accuracy is a scien-tific issue worthy of in-depth exploration.This paper introduces the idea of decomposition ensemble along with the theory of three-way decisions,and proposes a composite forecasting model based on three-way clustering.First,the Complementary Ensemble Empirical Mode Decomposition(CEEMD)method is used to decompose the original time series into several relatively stable sub-series,thereby reducing the complexity of the original time series while uncovering hidden information.Next,to address the different properties of the sub-series,sample entropy is used to measure the complexity of each sub-series,and a probabilistic rough set based on Bayesian risk decision is constructed to classify the sub-series into core,marginal,and trivial domains.Then,to avoid the lack of input information or interference from redundant information,a phase space reconstruction method is employed to determine the optimal input structures for Elman neural networks,extreme learning machines,and BP neural networks to predict the core,marginal,and trivial domains,respectively.Finally,the proposed model is applied to the prediction of ANY stock prices in the U.S.market,as well as to the prediction of important international and domestic stock indices and their constituent stocks.The method proposed in this paper demonstrates good predictive performance for stock prices,and its outstanding results can be attributed to the following factors:First,the CEEMD effectively uncovers hidden infor-mation in the time series.Second,three-way clustering enhances the adaptability of the forecasting method.Third,phase space reconstruction adaptively constructs the input structures of the neural networks.Theoretical-ly,the integration of granular computing with decomposition and integration methods represents a beneficial attempt and exploration in constructing complex dynamic data forecasting decision models and methods.From the perspective of time series complexity,the construction of a three-way clustering model based on Bayesian risk decision and probabilistic rough set offers a new perspective to enrich the theory of three-way decisions.In practice,achieving accurate stock price predictions can enable investors to more effectively avoid future risks and provide scientific support and reference for practical investment decisions.
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