Supervised classification of share price trends |
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Authors: | Zhanggui Zeng Hong Yan |
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Affiliation: | a School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia b Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong |
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Abstract: | Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm. |
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Keywords: | Singular spectrum analysis Share price data analysis Clustering algorithms Supervised pattern classification Naï ve Bayesian classifier |
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