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Supervised classification of share price trends

Authors
Journal
Information Sciences
0020-0255
Publisher
Elsevier
Publication Date
Volume
178
Issue
20
Identifiers
DOI: 10.1016/j.ins.2008.06.002
Keywords
  • Singular Spectrum Analysis
  • Share Price Data Analysis
  • Clustering Algorithms
  • Supervised Pattern Classification
  • Naïve Bayesian Classifier
Disciplines
  • Computer Science

Abstract

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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