Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems

The Applications in Stock Market of the Association Rules Data Mining Based on Time Series

Author YeXiang
Tutor ChengCongCong
School Nanchang University
Course Applied Computer Technology
Keywords data mining association rules time series stock bit strings
CLC TP311.13
Type Master's thesis
Year 2012
Downloads 220
Quotes 0
Download Dissertation

The researches of stock market behavior have always been issues of common concern to investors, and the research methods can be varied. This paper mainly used the association rules, an important branch of data mining, as a tool to mining the linkage between stocks, to count the occurrences of rising interval of three stocks statistically in the past within a certain time.If the occurrence rate was high, that is to say, the approval rate and confidence level was high, then when the situation as "A stock raises price on the date of Ta, B stock raises price on the date of Tb" occur, the investors could consider to buy a third stock on the date of Tc. The rule got by this way can be used to assist stock investment.The main content of this paper include the following aspects:Analyzing and summarizing the domestic literature which is about mining association rules on stock. The author conduct in-depth exploration into stock mining process, moreover the author conclude and evaluate some of the existing improved methods for data preprocessing, algorithm, association rules interestingness within the mining process.The investors would like to know the rules as "A stock raises price on the date of Ta, B stock raises price on the date of Tb. and from this we can get the information that C stock would raise price on the date of Tc.(Ta<Tb<Tc) The approval rate is X%, while the confidence level is Y%". Aimed at this, the author put forward two algorithm of mining association rules with time interval.Algorithm one is the algorithm of mining association rules based on time window. That is, applying a time window on the stock time series, and loop searching the two sets and three sets in time window, by moving the time window, find all of the two sets and three sets.Algorithm two is bit-search algorithm, introducing the concept of bit string to present the rising stock information within a continuous time series. As the bit strings is easy to carry on the shift operations and logic operations, it can greatly simplify the calculation of stock information and reduce the memory space needed. According to the result, whether the approval rate counts of strings meet the condition of minimum approval rate counts threshold or not, we can get the frequent two sets and three sets that we needed.(3) The design of stock association rule to mining model design and stock data preprocessing are introduced, and then the author puts bit-search algorithm into the application of stock data mining and conclude interaction rules between the stocks.

Related Dissertations
More Dissertations