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Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures

Hugueney, Bernard (2006), Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures, in Fürnkranz, Johannes; Scheffer, Tobias; Spiliopoulou, Myra, Knowledge Discovery in Databases: PKDD 2006 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings, Springer : Berlin, p. 545-552. http://dx.doi.org/10.1007/11871637_54

Type
Communication / Conférence
Date
2006
Conference title
10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2006)
Conference date
2006-09
Conference city
Berlin
Conference country
Allemagne
Book title
Knowledge Discovery in Databases: PKDD 2006 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings
Book author
Fürnkranz, Johannes; Scheffer, Tobias; Spiliopoulou, Myra
Publisher
Springer
Series title
Lecture Notes in Computer Science
Series number
4213
Published in
Berlin
ISBN
978-3-540-45374-1
Number of pages
660
Pages
545-552
Publication identifier
http://dx.doi.org/10.1007/11871637_54
Metadata
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Author(s)
Hugueney, Bernard
Abstract (EN)
Time series data-mining algorithms usually scale poorly with regard to dimensionality. Symbolic representations have proven to be a very effective way to reduce the dimensionality of time series even using simple aggregations over episodes of the same length and a fixed set of symbols. However, computing adaptive symbolic representations would enable more accurate representations of the dataset without compromising the dimensionality reduction. Therefore we propose a new generic framework to compute adaptive Segmentation Based Symbolic Representations (SBSR) of time series. SBSR can be applied to any model but we focus on piecewise constant models (SBSRL0) which are the most commonly used. SBSR are built by computing both the episode boundaries and the symbolic alphabet in order to minimize information loss of the resulting symbolic representation. We also propose a new distance measure for SBSRL0 tightly lower bounding the euclidean distance measure.
Subjects / Keywords
SBSRLO; SBSR

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