Hybrid Temporal Pattern Mining with Time Grain on Stock Index

Both sequential pattern mining and temporal pattern mining have become highly relevant data mining topics in this decade. In 2009, Wu and Chen proposed a representation for hybrid events and an HTPM mining method. However, their approach neither addresses nor analyzes the length of event time. An event representation may stand for the same event with extremely different time lengths, which may induces the loss of accurate mining results. This paper addresses this difficulty and explores different models and solutions. Firstly, this paper introduces the concept of the time grain, and proposes new hybrid models as well as the pattern mining algorithms associated with the concept of event length limit. Events in hybrid sequences are divided or distinguished according to a given threshold, to enable a detailed exploration of the more frequent hybrid sequence of events. Secondly, this paper utilizes the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the testing data, to examine the proposed model and the feasibility and effectiveness of the algorithm.