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IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied multivariate statistical analysis
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Machine Learning
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SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
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Information Sciences: an International Journal
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
Efficient approximation of Gaussian filters
IEEE Transactions on Signal Processing
A short text modeling method combining semantic and statistical information
Information Sciences: an International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
A time series forest for classification and feature extraction
Information Sciences: an International Journal
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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.