Knowledge and Information Systems

, Volume 3, Issue 3, pp 263–286 | Cite as

Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

  • Eamonn Keogh
  • Kaushik Chakrabarti
  • Michael Pazzani
  • Sharad Mehrotra

Abstract.

The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time.

Keywords: Data mining; Dimensionality reduction; Indexing and retrieval; Time series 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag London Limited 2001

Authors and Affiliations

  • Eamonn Keogh
    • 1
  • Kaushik Chakrabarti
    • 2
  • Michael Pazzani
    • 1
  • Sharad Mehrotra
    • 1
  1. 1.Department of Information and Computer Science, University of California, Irvine, CA, USAUS
  2. 2.Department of Computer Science, University of Illinois at Urbana Champaign, IL, USAUS

Personalised recommendations