Journal of the ACM (JACM)
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Finding Maximal Repetitions in a Word in Linear Time
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory
IEEE Transactions on Knowledge and Data Engineering
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Practical methods for constructing suffix trees
The VLDB Journal — The International Journal on Very Large Data Bases
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Complex Time-Series Data by Learning Markovian Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Adapting machine learning technique for periodicity detection in nucleosomal locations in sequences
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Mining bridging rules between conceptual clusters
Applied Intelligence
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
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Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise.