Tabu split and merge for the simplification of polygonal curves
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-resolution approach to time series retrieval
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
An incremental Hausdorff distance calculation algorithm
Proceedings of the VLDB Endowment
Classification of wrist pulse blood flow signal using time warp edit distance
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
ACM Computing Surveys (CSUR)
Rotation-invariant similarity in time series using bag-of-patterns representation
Journal of Intelligent Information Systems
Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
| Hi-index | 0.14 |
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 downsampled 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.