Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Boolean representation based data-adaptive correlation analysis over time series streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Modeling Aceto-White Temporal Patterns to Segment Colposcopic Images
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Adaptive correlation analysis in stream time series with sliding windows
Computers & Mathematics with Applications
An efficient time series data mining technique
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Periodic Pattern Analysis in Time Series Databases
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Interval-focused similarity search in time series databases
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Representing financial time series based on important extrema points
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Approximate clustering of time series using compact model-based descriptions
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Analysis of time series using compact model-based descriptions
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A review on time series data mining
Engineering Applications of Artificial Intelligence
Time series subsequence matching based on a combination of PIP and clipping
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
A multi-hierarchical representation for similarity measurement of time series
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Similarity search on time series based on threshold queries
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
ACM Computing Surveys (CSUR)
Finding time series discord based on bit representation clustering
Knowledge-Based Systems
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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Because time series are a ubiquitous and increasingly prevalent type of data, there has been much research effort devoted to time series data mining recently. As with all data mining problems, the key to effective and scalable algorithms is choosing the right representation of the data. Many high level representations of time series have been proposed for data mining. In this work, we introduce a new technique based on a bit level approximation of the data. The representation has several important advantages over existing techniques. One unique advantage is that it allows raw data to be directly compared to the reduced representation, while still guaranteeing lower bounds to Euclidean distance. This fact can be exploited to produce faster exact algorithms for similarly search. In addition, we demonstrate that our new representation allows time series clustering to scale to much larger datasets.