[Objective] This paper analyzes the differences in the importance of database items, aiming to address the issues of traditional association mining algorithm with redundant and worthless rules. [Methods] On the sequence with temporal constraints, we explored the non-weighted association rules with the frequency effective length and the weighting methods. Then, we used sliding window technique to study the rare weighted association rules on the time series. [Results] The accuracy of the prediction made by the proposed method increased to 69% from 62%. [Limitations] The mining algorithm took long time to extract the needed rules due to the sliding windows and the large number of rules generated. [Conclusions] The association rules of weighted time series improve the accuracy of recommendation, which also provides new directions for research method on association rules.
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