Data stream prediction based on rule antecedent occurrence tree matching

There are some shortages in the existing rule-based data stream prediction algorithm, such as inaccurate definition of antecedent occurrence, ignoring the correlation between rules and imprecise description of prediction accuracy. These make low forecasting process efficiency and low prediction accuracy. The superposed prediction algorithm was proposed based on antecedent occurrence tree, and interval minimal non-overlapping occurrence was defined to avoid the problem of excessive matching antecedent. The efficiency was improved for searching antecedent's occurrence by merging rule's antecedents in antecedent occurrence tree, and the succedent occurrence based on superposed probability was predicted to enhance prediction accuracy. The theoretical analysis and experimental evaluation demonstrate the algorithm is superior to the existing prediction algorithms in terms of time and space efficiency and prediction accuracy.