Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching

• Published in: IEEE transactions on pattern analysis and machine intelligence 2009-02, Vol.31 (2), p.306-318
• Main Author: Marteau, P.-F.
• Format: Article
• Language: English
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial Intelligence; Computer Science; Computer science; control theory; systems; Computer Simulation; Computer Society; Costs; Discrete transforms; Dynamic programming; Enterprise resource planning; Exact sciences and technology; Extraterrestrial measurements; Information Retrieval; Information systems. Data bases; Matching; Memory organisation. Data processing; Models, Theoretical; Monitoring; Particle measurements; Partitioning algorithms; Pattern recognition; Pattern Recognition, Automated - methods; Retrieval; Signal Processing, Computer-Assisted; Similarity; similarity measures; Software; Stiffness; Studies; Subtraction Technique; Tasks; Theoretical computing; Time Factors; Time measurement; Time series; Warp
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.76

In a way similar to the string-to-string correction problem, we address discrete time series similarity in light of a time-series-to-time-series-correction problem for which the similarity between two time series is measured as the minimum cost sequence of edit operations needed to transform one time series into another. To define the edit operations, we use the paradigm of a graphical editing process and end up with a dynamic programming algorithm that we call time warp edit distance (TWED). TWED is slightly different in form from dynamic time warping (DTW), longest common subsequence (LCSS), or edit distance with real penalty (ERP) algorithms. In particular, it highlights a parameter that controls a kind of stiffness of the elastic measure along the time axis. We show that the similarity provided by TWED is a potentially useful metric in time series retrieval applications since it could benefit from the triangular inequality property to speed up the retrieval process while tuning the parameters of the elastic measure. In that context, a lower bound is derived to link the matching of time series into down sampled representation spaces to the matching into the original space. The empiric quality of the TWED distance is evaluated on a simple classification task. Compared to edit distance, DTW, LCSS, and ERP, TWED has proved to be quite effective on the considered experimental task.