EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices

Li Tang, Ping He Pan, Yong Yi Yao


This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.


empirical mode decomposition, principal component analysis, affinity propagation, k-nearest neighbor, time series, stock index prediction

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DOI: https://doi.org/10.15837/ijccc.2018.2.3187

Copyright (c) 2018 LI TANG, Ping He Pan, Yong Yi Yao

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