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
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
DNA, Words and Models
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
GraphRank: Statistical Modeling and Mining of Significant Subgraphs in the Feature Space
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Computing exact P-values for DNA motifs
Bioinformatics
Discovering Significant Patterns
Machine Learning
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Computational Statistics & Data Analysis
Efficient exact motif discovery
Bioinformatics
Improving activity discovery with automatic neighborhood estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Finding Time Series Motifs in Disk-Resident Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
Effective connectivity analysis of fMRI data based on network motifs
The Journal of Supercomputing
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Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in the data mining communities to evaluate extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique—statistical tests—to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 35–53, 2012 © 2012 Wiley Periodicals, Inc.