The nature of statistical learning theory
The nature of statistical learning theory
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Stock market time series are inherently noisy. Although support vector machine has the noise-tolerant property, the noised data still affect the accuracy of classification. Compared with other studies only classify the movements of stock market into up-trend and down-trend which does not concern the noised data, this study uses wavelet soft-threshold de-noising model to classify the noised data into stochastic trend. In the experiment, we remove the stochastic trend data from the SSE Composite Index and get de-noised training data for SVM. Then we use the de-noised data to train SVM and to forecast the testing data. The hit ratio is 60.12%. Comparing with 54.25% hit ratio that is forecasted by noisy training data SVM, we enhance the forecasting performance.