Data Mining Of Time Series Based On Phase Space Reconstruction And Its Applications On Stock Market

Financial market sets up a connection between the demands of investment and funding. It could resolve the contradiction between supply and demand of capital effectively. Analysis methods of investment are always the researching hotspot of financial field. With the rapid developments of the financial market, there comes lots of creation and progress in investment analysis. Traditional time sires models have two disadvantages, which could not be avoided. The one is that it depends on several hypothesis conditions. The other is that applying overall fixed model to describe the economic or commercial time series structures, which are changed with times gradually, is not perfectly applicable.With the popularization of information technology in financial field and significant improvement of people's ability of collecting data, large amounts of data were accumulated, which were full of abundant information, while the rapid development of financial market. Data mining provides us new directions to analyze financial time series. Based on phase space reconstruction, This paper took time series as researching object to present time series data mining methods and apply these methods to financial market, in order to find the implicit rules, patterns and knowledge, so as to provide new directions, methods and accessorial information to market analysis and investment decision.Considering the researching background, this paper discussed the associated research of data mining technology, time series and financial time series data mining, separately. As following, the basic theory and methods of phase space reconstruction were analyzed in details. All of these provided the theoretical basis and technical feasibility to time series data mining based on phase space reconstruction.After contrasting the different means of time series pattern mining, we pointed out the problem of time series data mining framework TSDM, and presented the temporal patterns mining method based Wave Cluster systematically. By the multiresolution property of wavelet transformations and the grid-based partition method, it could detect arbitrary-shape clusters at different scales and levels of detail. We set up the investment strategy dictated by events that was predicted from temporal patterns and applied it to Chinese stock market. The result shows it would get the yield higher than buy-and-hold strategy. There is significant difference between the event series and non-event series. Mining temporal pattern could identify event effectively.After discussing the embedding theory and the time series forecasting, we improved original TS fuzzy neural network by means of EM (Expectation Maximization) method that is applicable to nonlinear space's clustering, and presented a new forecasting model of fuzzy neural network combined with Expectation Maximization method based on phase space reconstruction. It could cluster the data object and compute the membership automatically, to reduce the number of rules and simplify the structure of neural networks by applying EM method to the input reconstructed space. We used it to make forecasts on stock market. The results show that this model could reduce the error of forecasts effectively and improve the system's performance.We presented the sequential deviation detection method of time series derived from sequential outlier. We applied phase reconstruction CC method to estimate embedded dimension and embedded delay of time series and mapped time series into multi-dimension space. 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 it to Chinese stock market. The results show that, although in bear market, the strategy dictated by decision tree brought in considerable yield.This paper divided time series into the sub-series set which had the same length and mapped all these sub-series into multidimensional space, so as to turn the one dimensional ordered data problem of time series into data mining on out-of-order data sets of multidimensional space. The researches of the paper provided not only new methods to financial time series analysis, but also new directions to data mining research.