Uncertainty Temporal Data Mining Method And Its Application In Forecasting The Securities Markets Quotations

With the development of financial globalization and liberalization, the efficiency of the financial sector determines the level of a country's economic competitiveness at a large extent, and information technology is one of the important factors to impact the financial industry innovation ability and development level. Many financial structures use more advanced information technology and intelligent decision support technology to find out useful rules by analyzing massive data, which are accumulated in operational systems. As a new intelligent decision support technology, data mining technology has been used in some fields of financial sector. Based on these conditions, how to mining more useful information from financial data to understand, master, and use its own rules and undoubtedly has special significance.The information of financial market has the characteristicses of uncertainty, nonlinear, fuzzy nature of the information data and non-structure. The uncertainty in financial market, not only includes the uncertainty of time, but also the uncertainty of information and technology. All of these are worthy of in depth research.Uncertainty methods and data mining technology have similar usages to some extent, but they have some limitations when we use them alone. As we know, it has uncertainty in data mining, and the analysis of financial time series also has uncertainty problems. Furthermore, traditional mathematical statistics motheds don't fit to find potential rules from large amounts of data. In this paper, we mainly study uncertain knowledge and data mining technology to propose a new method-uncertainty temporal data mining method (UTDM), we can obtain a series of new methods from UTDM, and use the new methods to solve the problems we meet during the mining and fortcasting processes. We use these methods to study securities markets, primarily for the securities markets'trends, stock price forecasts, and stock index forecasts.We choose some typical methods in uncertainty methods and data mining technology to setup useful methods to analysis securities markets: fuzzy sets, fuzzy rough sets based on fuzzy similar relationship, gray theory, association rules, networks method. We summarize the related theories and methods of uncertain knowledge, data mining technology and financial time series, point out the limitations of the current studies, lay the foundation for uncertainty temporal data mining method, clarify the objections of this study, and give us important informations to make the further theoretical research.We use fuzzy similar relationship through the rough sets and fuzzy data mining techniques to predict stock prices, obtain strong rules from the securities markets and economic data. We use fuzzy rough sets and data mining technology to forecast stock price at certain given time. At first, we use fuzzy sets and rough sets to make the stock price into some groups based on the attribute"time", and then compute each true value by the fuzzy rough sets, we can obtain the candidate properties by data mining method. In the end, we can get the useful rules and forecast the change trading of stock price in a certain time. The result of the method we pointed out is more exact than other method. In this paper, we share the time series into a variable rate of change in prices of the time series analysis, improve the trend of feature extraction and clustering algorithm, transfore time series prediction into frequent and effective feature sets to discover problems to mining forecast rules, judge the market trends according to the change in market during a period of time.In order to assist stock investors to make reasonable decision, we use fuzzy association rules to mining securities markets'exchange rules in this paper. Firstly, we disperse the fuzzy sets'attributes by clustering method. Secondly, fuzzy sets and their corresponding membership function of the quantitative attributes are generated by means of the mediods. Finally, we give the algorithm a name which is called FARS. At the end of the 6th chapter, we study the fuzzy association rules based on temporal forms. We combine three gray models: residual GM(1, 1), unbiased GM(1, 1), pGM(1, 1) and neural network to propose a new combination forecasting model. And use it to make forecasts on Composite Stock Price Index of the securities markets in Shanghai, China. The results show that this model could gain optimized forecasting value and could be taken as an effective tool to predict Shares Price Composite Index. The model makes full use of grey prediction modeling information which requires less information and the neural network has strong ability and good nonlinear mapping and fault-tolerant, self-organizing and adaptive characteristics. The forecasts on Composite Stock Price Index of the securities markets in China shows that the combination of the effectiveness of prediction accuracy.