Stock Trend Analysis About Listed Ompanies In The IT With Time-series Association Rules

Data mining association rules is a very important research topic, which essence is revealed hidden dependencies between the objects in a large number of data set, according to this dependency information from an object to infer information about related objects. With stock time series data using data mining techniques to explore its excavation a potentially valuable pattern, theoretical study and practical guidance is of great significance.First, the data mining association rule mining algorithm was analyzed, and then focus on two aspects of the work carried out as follows:①After transforming source data to the discrete time series (symbol) of transaction data on which traditional classical Apriori algorithm is used, inter-linked shares association rules about the IT is modeled, and design detail of the algorithm model is predicted.②By using time series similarity searching method, historical stock data is found out according to the similarity about trends. Following smooth the time series after normalization, by clustering with Euclidean distance the frequent time series fragments are found, on which time series fragments frequent association rules are analyzed, learning directly from the data pattern of frequent occurrence, and use this model to trend forecasting. Based on the above, application of prototype system to 102 stocks in the IT section of Shanghai and Shenzhen A shares from March 2007 to July 2009 closing stock price as the test set, the time sequence of the IT frequent fragment mining association rules model predicts observing systems in the analysis from a large number of search results matching the results found in the same strings with the historical data corresponding to a large degree of similarity, which shows changes in the future the stock market that will be in the past to reproduce a certain stage of history. That demonstrates the method is simple and effective. Comparing with the traditional association rules algorithm it is more suitable for stock forecasting, and can direct good results for a number of sequences with delay. On the establishment of the corresponding prototype system, the corresponding algorithm has been implemented.