On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Proceedings of the VLDB Endowment
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Process-oriented estimation of generalization error
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
uWave: Accelerometer-based personalized gesture recognition and its applications
Pervasive and Mobile Computing
Fast approximate correlation for massive time-series data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data Mining and Knowledge Discovery
Motion primitive-based human activity recognition using a bag-of-features approach
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
A shapelet transform for time series classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation-invariant similarity in time series using bag-of-patterns representation
Journal of Intelligent Information Systems
Towards never-ending learning from time series streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering longest-lasting correlation in sequence databases
Proceedings of the VLDB Endowment
Classification of time series by shapelet transformation
Data Mining and Knowledge Discovery
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Time series shapelets are small, local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers and for certain visualization and summarization tasks. While shapelets were introduced only recently, they have already seen significant adoption and extension in the community. Despite their immense potential as a data mining primitive, there are two important limitations of shapelets. First, their expressiveness is limited to simple binary presence/absence questions. Second, even though shapelets are computed offline, the time taken to compute them is significant. In this work, we address the latter problem by introducing a novel algorithm that finds shapelets in less time than current methods by an order of magnitude. Our algorithm is based on intelligent caching and reuse of computations, and the admissible pruning of the search space. Because our algorithm is so fast, it creates an opportunity to consider more expressive shapelet queries. In particular, we show for the first time an augmented shapelet representation that distinguishes the data based on conjunctions or disjunctions of shapelets. We call our novel representation Logical-Shapelets. We demonstrate the efficiency of our approach on the classic benchmark datasets used for these problems, and show several case studies where logical shapelets significantly outperform the original shapelet representation and other time series classification techniques. We demonstrate the utility of our ideas in domains as diverse as gesture recognition, robotics, and biometrics.