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Eamonn Keogh, K. Chakrabarti, S. Mehrotra, M. Pazzani (2001)
Locally adaptive dimensionality reduction for indexing large time series databases, 30
(2004)
Mining the stock market : which measure is best ? Every - thing you know about dynamic time warping is wrong
Martin Gavrilov, Dragomir Anguelov, P. Indyk, R. Motwani (2000)
Mining the stock market (extended abstract): which measure is best?
Dina Goldin, P. Kanellakis (1995)
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
Gautam Das, King-Ip Lin, H. Mannila, Gopal Renganathan, Padhraic Smyth (1998)
Rule Discovery from Time Series
D. Berndt, J. Clifford (1994)
Using Dynamic Time Warping to Find Patterns in Time Series
Jessica Lin, Eamonn Keogh, S. Lonardi, B. Chiu (2003)
A symbolic representation of time series, with implications for streaming algorithms
K. Chan, A. Fu (1999)
Efficient time series matching by waveletsProceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)
Lei Chen, R. Ng (2004)
On The Marriage of Lp-norms and Edit Distance
A. Bagnall, G. Janacek (2004)
Clustering time series from ARMA models with clipped dataProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
V. Megalooikonomou, Qiang Wang, Guo Li, C. Faloutsos (2005)
A multiresolution symbolic representation of time series21st International Conference on Data Engineering (ICDE'05)
C. Ratanamahatana, Eamonn Keogh (2004)
Everything you know about Dynamic Time Warping is Wrong
R. Agrawal, C. Faloutsos, A. Swami (1993)
Efficient Similarity Search In Sequence Databases
Lei Chen, M. Özsu, Vincent Oria (2005)
Robust and fast similarity search for moving object trajectories
Byoung-Kee Yi, C. Faloutsos (2000)
Fast Time Sequence Indexing for Arbitrary Lp Norms
Eamonn Keogh, M. Pazzani (2000)
Scaling up dynamic time warping for datamining applications
Flip Korn, H. Jagadish, C. Faloutsos (1997)
Efficiently supporting ad hoc queries in large datasets of time sequences
Eamonn Keogh, M. Pazzani (1998)
An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback
Gautam Das, D. Gunopulos, H. Mannila (1997)
Finding Similar Time Series
Martin Gavrilov, Dragomir Anguelov, P. Indyk, R. Motwani (2000)
Mining The Stock Market : Which Measure Is Best ?
Similarity query is a frequent subroutine in time series database to find the similar time series of the given one. In this process, similarity measure plays a very important part. The previous methods do not consider the relation between point correspondences and the importance (role) of the points on the content of time series during measuring similarity, resulting in their low accuracies in many real applications. In the paper, we propose a General Hierarchical Model (GHM), which determines the point correspondences by the hierarchies of points. It partitions the points of time series into different hierarchies, and then the points are restricted to be compared with the ones in the same hierarchy. The practical methods can be implemented based on the model with any real requirements, e.g. FFT Hierarchical Measures (FHM) given in this paper. And the hierarchical filtering methods of GHM are provided for range and k -NN queries respectively. Finally, two common data sets were used in k -NN query and clustering experiments to test the effectiveness of our approach and others. The time performance comparisons of all the tested methods were performed using the synthetic data set with various sizes. The experimental results show the superiority of our approach over the competitors. And we also give the experimental powers of the filtering methods proposed in the queries.
ACM SIGMOD Record – Association for Computing Machinery
Published: Mar 1, 2007
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