Algorithms for clustering data
Algorithms for clustering data
Fundamentals of speech recognition
Fundamentals of speech recognition
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A time series representation model for accurate and fast similarity detection
Pattern Recognition
MaSDA: A system for analyzing mass spectrometry data
Computer Methods and Programs in Biomedicine
A time-dependent enhanced support vector machine for time series regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We present DSA - Derivative time series Segment Approximation, a novel representation model for time series designed for effective and efficient similarity search. DSA substantially exploits derivative estimation, segmentation and dimensionality reduction to meet at least the requirements of high sensitivity to main features (trends) of time series and robustness to outliers. Experiments show that DSA is drastically faster and still as good or better than the prominent state-of-the-art similarity methods.