Text algorithms
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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 periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Discovery of Group Lag Correlations in Streams
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
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
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. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm out-performs the existing periodicity detection algorithms in terms of noise resiliency.