A Study On Prediction Of Stock Time Series Trend Based On Association Rules

The Stock index of china has been grow up from about 1100 to 6000 since From January 2006 to June 2007 . This situation is rare in the world. Although it contain huge change of rise from bottom, but it also make most of the investor to the great bad. With the economic growth and the conversion of people's investmentconsciousness, the stock has become a more and more important part of economy. The investment in stock has become one of focuses of public topic. How to keep the development and boom of stock market is becoming the emphasis of concern and research of manager and investor. The proceeds of stock investment always equal to the risk. That means the good proceeds is based on the poor risk of failure. Therefore the study of stock prediction method has great application value and theoretical significance. Traditional technical analysis and fundamental analysis also have their respective advantages and disadvantages .our stock market is becoming more mature day by day. This paper try to find a way to apply data mining technology into stock analysis to help investor get more information of the stock and enhance the analysis and judgement for some share, because there has no trusty way to predict stock market. Nowadays, we can use lots of statistical method of analysis to discover some concealed rules in stock information, thereby help investors to analyze and forecast the stock.Exploration of algorithms plays an important role in all Data Mining research. Data Mining faces large database. The efficiency of algorithms is the most important, so it is very significant to research and improve the existing algorithms. Based on above, this thesis mainly studies the algorithms of association rule mining.Firstly, it generally introduces Data Mining and the basis of the stock of knowledge, including the concepts and the patterns, main mining problems, system classifications, and the application and development trend. Secondly, this thesis researches the Association Rule Algorithm totally, which is important in Data Mining. It analyses the classical algorithms that are Apriori,AprioriTid and the improved algorithms of Apriori of the stock data. It summarizes existing problems in these algorithms. Then this thesis presents an improved OptimizedApriori algorithm - Dual transaction analysis in Stock Time Series Association Rules Based on trading volume and Two-dimensional time mode, which is one of the key contents. In order to discovery the stock market information well, we must combine stock market characteristic, especially operational rules of stock itself. The movement of stock includes thinking and wisdom of tens of thousands of people. We want to study it only through detailed and patient observation. By a long time studying and tracking stock market and simulated operation to look for some rulus of time constraint as follows: if the closing price of stock A is going up to 2% and its trading volume is greater than vol_min(a preset Threshold) in a time——segment W (suck as one day),then those of stock B and C will also rise (or descent) in 80% probability in the time——segment(that is the third day) just after INT_DAY time——segments(such as two days).Finaly the disposal of stock data, the improvement of algorithm and mining were completed under VC++6.0 platform. The experiments show that the eficiency of the improved OptimizedApriori algorithm was superior to OptimizedApriori algorithm to a certain extent. And a lot of association rules were extracted, some of them have fine instructional significance.