Dictionary-Based Compression for Long Time-Series Similarity

—Long time-series datasets 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 ...

• Type: Journal
• Year: 2010
• Where: TKDE
• Authors: Willis Lang, Michael D. Morse, Jignesh M. Patel
• Keywords: Time-series, Time-series Datasets, Time-series Similarity, TKDE 2010