The Model Of Stock Sequence With Time Constraint And The Research Of Mining Algorithm

With the developing of market-directed economy, our stock market is becoming more mature and standardize day by day, and the investor's decision is more rational. 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.However, these common statistical method of analysis can't be used to find out the rule with time constraint in stock market as follows, if the closing price of stock A is going up to 5% 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 time-segments (such as two days). Therefore, in this paper a new developing technique-data mining (DM) is adopted to look for these compound sequence rules in stock market. No doubt, the mining of the sequence rule with two dimensions-time constraints has very important meaning in guiding investment decision.In this paper, there are three innovations. The first innovation is that we construct two stock sequence rule models with time constraint: the stock' sequence rule model of one dimension with certain time-segment (represented by W) constraint and the stock' sequence rule model of two dimensions with W and time-interval (represented by INT) constraints. And the second innovation is that we bring about the mining of the stock' sequence rule of one dimension through extending the association rule algorithm - Apriori algorithm and FP_Growth algorithm. So as to the third innovation is designing a new algorithm to mine the stock' sequence rule of two dimensions. And we also validate the feasibility of the algorithms given by the paper through a positive research in the last chapter of the paper.This paper includes four parts. In the first part we introduce some basic concepts of the technology of data mining and the traditional analytic methods of stock. In the second part we establish two models for mining the stock' sequential scheme with time constraint. Then we accomplish the mining algorithm of stock' sequence rule of one dimension and two dimensions with time constraint, and we also extendly discuss the problems that should be paid attention to in order to achieve the sequence rule in a distributed system in the third part. In the last part, we make a positive research to verify the correctness of the algorithms given by the paper.