SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "i...

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Bibliographic Details
Published inProceedings (IEEE International Conference on Data Mining) pp. 1175 - 1180
Main Authors Senin, Pavel, Malinchik, Sergey
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
Aggregates
Approximation
Approximation algorithms
Approximation methods
Classification
classification algorithms
Conferences
Data mining
Euclidean distance
Learning
Mathematical analysis
Time series
Time series analysis
Training
Vector spaces
Vectors
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Summary:In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approximation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their "importance" to the class, which not only facilitates well-performing classification procedure, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1550-4786
DOI:10.1109/ICDM.2013.52