Mining asynchronous periodic patterns in time series data

Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min rep and max dis are employed to specify the minimum number of repetitions that is required within each segment of non-disrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid su...

• Type: Conference
• Year: 2000
• Where: KDD
• Authors: Jiong Yang, Wei Wang 0010, Philip S. Yu
• Keywords: Asynchronous Periodic Pattern, Data Mining, KDD 2000, Longest Valid Subsequence, Periodic Pattern