Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Matching and indexing sequences of different lengths
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Piecewise linear correction of ECG baseline wander: a curve simplification approach
Computer Methods and Programs in Biomedicine
Pattern Recognition
Reduced data similarity-based matching for time series patterns alignment
Pattern Recognition Letters
| Hi-index | 0.10 |
We propose a novel method for quasi-periodic time series patterns matching, through signature exchange between the two patterns. The signature is obtained through sorting of the time series on magnitude. The advantage is that the difficult task of comparing the two patterns can be easily performed as a result of this exchange: The original time series is compared (point to point matching) to the reconstructed time series obtained through the reverse process, using the other time series signature. The matching is such that periods in one time series are put into correspondence with periods in the other time series, even if the time series is of different basic patterns and/or different lengths. The method is simple to implement and requires no parameters. It was compared to the very appreciated DTW algorithm on execution time, space and accuracy. Due to the quasi-periodic nature of the electrocardiogram, the tests were performed on ECG traces, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MITBIH) public database. Results show that the proposed method outperforms DTW on all aspects. This suggests that our method could be a good alternative to the classical DTW technique for quasi-periodic signals comparison. Specific applications are foreseen for our method: Novelty detection and person identification using ECG.