Based On Data Mining Of Financial Time Series Wavelet Theory Is Applied

Data mining is derived from business demand, so it has popular value in all kinds of business filed. We will distill the high interest degree data by data mining from large history stock data. Then the data was analyzed, and was forecasted by mathematic tools, and researching the trend of the stock data, which is becoming the focus of finance academe.We can further to the research of time-varying self-similar exponent, simulation and forecast wave, stock index, so we can mine effective potential information from raw financial data. These data were used on many field, which include bottle-neck of resolving knowledge explode and getting knowledge, providing exact> timely and full decision-making information.In order to provide theory foundation for financial market analysis, prediction and regulation, the author results thoroughly in financial time series by using data mining and wavelet. The summary as follows:Data distill by k-means analyze technology based on genetic algorithm. The clustering analyze includes: data discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes. The methods focus on not only the distance of two objects but also potential describe of class.It cannot make sure the distribution and location of regular periodic change. The author put forward a new detecting method in financial time series, which based on data mining and wavelet theory. The positive researches have been studied in Nasdaq stock market and the results show that the stock market has the character of remarkable period-dominated.