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Time series analysis with multiple resolutions

Authors
Journal
Information Systems
0306-4379
Publisher
Elsevier
Publication Date
Volume
35
Issue
1
Identifiers
DOI: 10.1016/j.is.2009.03.006
Keywords
  • Time Series
  • Vector Quantization
  • Multiple Resolutions
  • Similarity Search

Abstract

Abstract We introduce a new representation for time series, the Multiresolution Vector Quantized (MVQ) approximation, along with a distance function. Similar to Discrete Wavelet Transform, MVQ keeps both local and global information about the data. However, instead of keeping low-level time series values, it maintains high-level feature information (key subsequences), facilitating the introduction of more meaningful similarity measures. The method is fast and scales linearly with the database size and dimensionality. Contrary to previous methods, the vast majority of which use the Euclidean distance, MVQ uses a multiresolution/hierarchical distance function. In our experiments, the proposed technique consistently outperforms the other major methods.

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