Sequence Outlier Detection Based on Chaos Theory and Its Application on Stock Market
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.
KeywordsTrading Strategy Outlier Detection Chaos Theory Large Lyapunov Exponent Chaotic Time Series
Unable to display preview. Download preview PDF.
- 1.Box, G.E.P., Jenkins, G.M.: Time series Analysis: Forecasting and Control[M]. Halden-Day, San Francisco (1976)Google Scholar
- 5.Takens, F.: Detecting Strange Attractors in Turbulence. Lecture Note in Mathematics, pp. 366–381 (1980)Google Scholar
- 10.Grassberger, P., Procaccia, I.: Phys. Rev. Lett., vol. 50, p. 345 (1983)Google Scholar
- 11.Brock, W.A., Hsieh, D.A., LeBaron, B.: Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence. MIT Press, Cambridge (1991)Google Scholar
- 12.Han, J., Kamber, M.: Data Mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
- 13.Arning, A., Agrawal, R., Raghavan, P.: A linear method for deviation detection in large databases. In: Proc. 1996 int. conf. Data Mining and Knowledge Discovery, Philadelphia, PA, pp. 164–169 (1999)Google Scholar
- 14.Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1) (1986)Google Scholar
- 15.Quinlan, J.R.: C4.5: Program of Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 17.Povinelli, R.J.: Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events. Temporal, Spatial and Spatio-Temporal Data Mining, 46–61 (2000)Google Scholar