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Computational intelligence (CI) approaches such as neural networks (NNs) and neuro-fuzzy approaches have been used for stock price forecasting. Robust and efficient stock market models can achieve more accurate predictions and decision making for individual investors or stock fund managers. This work thus surveys individual and hybrid CI methods, including a self-organizing polynomial neural network...
In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA) is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
In this study, the application of independent component analysis (ICA), a new feature extraction method, and support vector regression (SVR) in time series prediction is presented. The proposed method first use ICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features...
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