As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

Financial time series forecasting using independent component analysis and support vector regression

A study of Gaussian mixture models of color and texture features for image classification and segmentation

Robust twin support vector machine for pattern classification

Automatic recognition of five types of white blood cells in peripheral blood

Feature selection and instance selection are two important data preprocessing steps in data mining, where the former is aimed at removing some irrelevant and/or redundant features from a given dataset and the latter at discarding the faulty data. Genetic algorithms have been widely used for these tasks in related studies. However, these two data preprocessing tasks are generally considered separately in literature. It is unknown what the performance differences would be when feature and instance selection and feature or instance selection are performed individually. Therefore, the aim of this study is to perform feature selection and instance selection based on genetic algorithms using different priorities to examine the classification performances over different domain datasets. The experimental results obtained from four small and large scale datasets containing various numbers of features and data samples show that performing both feature and instance selection usually make the classifiers (i.e., support vector machines and k-nearest neighbor) perform slightly poorer than feature selection or instance selection individually. However, while there is not a significant difference in classification accuracy between these different data preprocessing methods, the combination of feature and instance selection largely reduces the computational effort of training the classifiers, as opposed to performing feature and instance selection individually. Considering both classification effectiveness and efficiency, we demonstrate that performing feature selection first and instance selection second is the optimal solution for data preprocessing in data mining. Both SVM and k-NN classifiers provide similar classification accuracy to the baselines (i.e., those without data preprocessing). The decisions regarding which data preprocessing task to perform for different dataset scales are also discussed.

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Hailong Zhu

Papers

Texture classification using the support vector machines

Signal de-noising by improving soft thresholding on the dyadic wavelet transform