The Application Of Stream Data Time-Series Pattern Reliance Mining In Stock Market Analysis

With the rapid development of computer information technology, a new data model-the stream data emerged. The stream data is coming continuously, quickly, changing with time, and may be unpredictable and unlimited in the way. At present, the stream data has been widely used in telecommunications, financial securities, retail trade and other fields. The emergence of stream data has brought new challenges from the nature of the data to static database technology and data mining technology.As stream data is unlimited, it is impossible to store all the data for the arrival of so large amounts of data, many traditional data mining algorithms are not suitable for stream data mining. The thesis proposes a mining model and algorithm of stream data time-series pattern reliance in a dynamic stock market through the research of stream data time-series pattern mining algorithms and applications, and then does short-term forecasts on the stocks with pattern reliance.Firstly, this paper analyzes the feasibility of the stock stream data time-series pattern reliance mining; builds the basic model of the time-series pattern reliance mining, and then designs the time-series pattern reliance mining algorithm on the mining model and proposes a time-series pattern reliance mining algorithm in stock market analysis based on the sliding window and the summary data structure, finally, the model does short-term forecasts on the stocks with pattern reliance to provide rational guidance for stock investors. This algorithm meets the stock data mining requirements, it can adapt to the data non-linear and real-time dynamic changing features effectively, so as to mine time-series pattern effectively and satisfy real-time demands and analysis of the stock market for users.