New Time Series Data Representation ESAX for Financial Applications

Authors:
Battuguldur Lkhagva;Yu Suzuki;Kyoji Kawagoe
Affiliations:
Ritsumeikan University, Japan;Ritsumeikan University, Japan;Ritsumeikan University, Japan
Venue:
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Year:
2006

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Abstract

Efficient and accurate similarity searching for a large amount of time series data set is an important but non-trivial problem. Many dimensionality reduction techniques have been proposed for effective representation of time series data in order to realize such similarity searching, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), the Adaptive Piecewise Constant Approximation (APCA), and the recently proposed Symbolic Aggregate Approximation (SAX).