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Issue No.11 - November (2010 vol.22)
pp: 1609-1622
Willis Lang , University of Michigan, Ann Arbor
Michael Morse , University of Michigan, Ann Arbor
Jignesh M. Patel , University of Michigan, Ann Arbor
Long time-series data sets are common in many domains, especially scientific domains. Applications in these fields often require comparing trajectories using similarity measures. Existing methods perform well for short time series but their evaluation cost degrades rapidly for longer time series. In this work, we develop a new time-series similarity measure called the Dictionary Compression Score (DCS) for determining time-series similarity. We also show that this method allows us to accurately and quickly calculate similarity for both short and long time series. We use the well-known Kolmogorov Complexity in information theory and the Lempel-Ziv compression framework as a basis to calculate similarity scores. We show that off-the-shelf compressors do not fair well for computing time-series similarity. To address this problem, we developed a novel dictionary-based compression technique to compute time-series similarity. We also develop heuristics to automatically identify suitable parameters for our method, thus, removing the task of parameter tuning found in other existing methods. We have extensively compared DCS with existing similarity methods for classification. Our experimental evaluation shows that for long time-series data sets, DCS is accurate, and it is also significantly faster than existing methods.
Spatial databases and GIS, database management.
Willis Lang, Michael Morse, Jignesh M. Patel, "Dictionary-Based Compression for Long Time-Series Similarity", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 11, pp. 1609-1622, November 2010, doi:10.1109/TKDE.2009.201
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