SIAM Journal on Computing
Introduction to algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
External memory algorithms and data structures: dealing with massive data
ACM Computing Surveys (CSUR)
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Incremental, Online, and Merge Mining of Partial Periodic Patterns in Time-Series Databases
IEEE Transactions on Knowledge and Data Engineering
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Finding calendar-based periodic patterns
Pattern Recognition Letters
RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Mining Regular Patterns in Transactional Databases
IEICE - Transactions on Information and Systems
Mining periodic patterns in spatio-temporal sequences at different time granularities
Intelligent Data Analysis
Modeling knowledge discovery in financial forecasting
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Mining Calendar-Based Periodicities of Patterns in Temporal Data
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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 periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis on repeat-buying patterns
Knowledge-Based Systems
A review on time series data mining
Engineering Applications of Artificial Intelligence
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
A parallel algorithm for mining multiple partial periodic patterns
Information Sciences: an International Journal
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Mining temporal patterns in popularity of web items
Information Sciences: an International Journal
Mining event periodicity from incomplete observations
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
Efficient health data compression on mobile devices
Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare
Mining sequential patterns with extensible knowledge representation
Intelligent Data Analysis
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n\log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.