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There are two problems as we use conventional Boolean association rules mining algorithm to discover temporal association rules over the stock market to predict stock price variation. The first problem is that the discovered rules only consider associations between the presence and absence of variations of stock prices and the second problem is that the associations among stock price variations are within the same transaction day. For example, if stock A raises, then stock B raises the same day. This Boolean temporal association rule reveals no information of quantitative variations of stock prices and can only predict price trend in the same day. In this paper, we deal with the problem of mining temporal association rules in stock databases containing quantitative price variations to discover the associations among different transactions day. Our algorithm first employs data discretization concept to partition quantitative attributes into intervals and an adaptive a priori method that cooperates with time sliding window concept and prefix tree is developed to find quantitative temporal association rules. An example of such a rule might be "if stock A price variation raised 5% to 7% and stock B raised 2.5% to 5% the same day, then stock C will raise 0% to 2.5% in the next two days." In this case, the stock price variation is taking into consideration and the associated stock price variations belong to different transaction days. As compared with conventional methods, more useful results can be found from the proposed quantitative temporal association rules.