Discovery of Periodic Patterns in Sequence Data: A Variance-Based Approach

Authors:
Yinghui (Catherine) Yang;Balaji Padmanabhan;Hongyan Liu;Xiaoyu Wang
Affiliations:
Graduate School of Management, University of California, Davis, Davis, California 95616;Information Systems and Decision Sciences, College of Business, University of South Florida, Tampa, Florida 33620;Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing, China 100084;Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing, China 100084
Venue:
INFORMS Journal on Computing
Year:
2012

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Abstract

We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of statistical variance to define periodicity. This lends itself to efficient variance-reduction algorithms for identifying periodic patterns. We motivate and test our approach using both extensive simulated sequences and real sequence data from online clickstream data.