Abstract
There are many observable factors that could influence and determine the time series. The dynamic equations of their interaction are always nonlinear, sometimes chaotic. This paper applied phase space reconstruction method to map time series into multi-dimension space based on chaos theory. Extracted from multi-dimension phase space by the method of sequential deviation detection, outlier set was used to construct a decision tree in order to identify the kinds of outliers. According to the results of decision tree, a trading strategy was set up and applied to Chinese stock market. The results show that, even in bear market, the strategy dictated by decision tree brought in considerable yield.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xie, C., Chen, Z., Yu, X. (2006). Sequence Outlier Detection Based on Chaos Theory and Its Application on Stock Market. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_153
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DOI: https://doi.org/10.1007/11881599_153
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
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