The Application Research Of Time Sequence Data Mining In Financial Domain

The Knowledge Discovery in Database combined with database technique, the artificial intelligence and other professional domain knowledges, is becoming a hotspot in computer science in recent years. Currently, the KDD research has related to several aspects such as: time sequence rules, association rules, classification rules and clusters rules. All aspects are obtained good results. In practical works, such as online processing analysis, the data warehouse, KDD got the extensive application. With the rapid development of network technique, people attach more and more important to the research of KDD in Web.This disquisition focus on the research of KDD in time sequence data, acquires the inside regulations and use for financial domain.There are a great deal of data in financial domain. The data quantity is so huge that we can not find knowledges by tradition methods. It's need new knowledge and technology to resolve this problem. In financial, KDD is mainly used to analysis the custom relationship management. There hasn't many KDD method to be used in transaction data. In the actual work, it request a kind of tool to analysis transaction data, discover it's inside regulations, thus to judge these data's quality and tendency.This disquisition aims at the KDD application in financial sequent data, discover a fit pattern, and design a testing system to predict the data tendence. In expectation, it can rise a certain impetus in financial realm.The followings are results of our research:First, we proposed an applying system frame that can preprocess data, find and evaluate patterns, analysis and estimate data with financial domain knowledges. It helps people to make deeper understanding for the inside regulations and characteristics in data.Second, aiming at the financial time sequence data, we proposed a method to improve C-mean algorithm. So, it can find patterns automatically.Third, in the phase of forecasting, according with the pattern matching, we can get small sample fields. Instead of judgement which based on curve trend, we can get better estimate result by use the regression function in that small sample field.