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Abstract: The problem of finding patterns of interest in time series databases (query by content) is an important one, with applications in virtually every field of science. A variety of approaches have been suggested. These approaches are robust to noise, offset translation, and amplitude scaling to varying degrees. However, they are all extremely sensitive to scaling in the time axis (longitudinal scaling). We present a method for similarity search that is robust to scaling in the time axis, in... (Update)
Context of citations to this paper: More
...method for sequence retrieval, where the features extracted are characteristic parts of the sequence, so called feature shapes. In [13] Keogh uses a similar landmark based technique. Both these methods also use dimensionality reduction technique of piecewise linear...
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0.6: An Indexing Scheme for Fast Similarity Search in Large Time.. - Keogh, Pazzani (1999) (Correct)
0.3: A Probabilistic Approach to Fast Pattern Matching in Time.. - Keogh, Smyth (1997) (Correct)
0.2: Bursty and Hierarchical Structure in Streams - Kleinberg (2002) (Correct)
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0.3: Iterative Deepening Dynamic Time Warping for Time Series - Chu, Keogh, Hart, Pazzani (2002) (Correct)
0.2: Mining Motifs in Massive Time Series Databases - Patel, Keogh, Lin, Lonardi (2002) (Correct)
0.1: A Simple Dimensionality Reduction Technique for Fast.. - Keogh, Pazzani (2000) (Correct)
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7: An enhanced representation of time series which allows fast and accurate classif.. - Keogh, Pazzani - 1998
6: A probabilistic approach to fast pattern matching in time series databases - Keogh, Smyth - 1997
5: Scaling up Dynamic Time Warping to Massive Datasets - Keogh, Pazzani - 1999
BibTeX entry: (Update)
E. Keogh, "A Fast and Robust Method for Pattern Matching in Time Series Databases," proceedings of 9th International Conference on Tools with Artificial Intelligence (TAI '97), 1997. http://citeseer.ist.psu.edu/keogh97fast.html More
@inproceedings{ keogh97fast,
author = "Eamonn J. Keogh",
title = "Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases",
booktitle = "{ICTAI}",
pages = "578-584",
year = "1997",
url = "citeseer.ist.psu.edu/keogh97fast.html" }
Citations (may not include all citations):241 Fast Subsequence Matching in Time-Series Databases - Faloutsos, Ranganathan et al. - 1994
205 Efficient Similarity Search in Sequence Databases - Agrawal, Faloutsos et al. - 1993
126 Fast Similarity Search in the Presence of Noise (context) - Agrawal, Lin et al. - 1995
50 Approximate Queries and Representations for Large Data seque.. - Hagit, Zdonik - 1996
49 Probabilistic Approach to Fast Pattern Matching in Time Seri.. - Keogh, Smyth - 1964
31 Segmentation of Plane Curves (context) - Pavlidis, Horowitz - 1974
29 Using Dynamic Time Warping to Find Patterns in Time Series (context) - Berndt, Clifford - 1994
12 A System for Approximate Tree Matching (context) - Wang, Zhang et al. - 1994
12 Waveform Segmentation Through Functional Approximation (context) - Pavlidis - 1976
10 Structural Processing of Waveforms as Trees (context) - Shaw, DeFigueiredo
7 Representation of Random Waveforms by Relational Trees (context) - Ehrich, Foith - 1976
2 Waveform Correlation using Tree Matching (context) - Cheng, Lu - 1982
1 Recognition of Planer Class Objects (context) - Burl, Perona - 1996
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.ics.uci.edu/~eamonn/): More
An Enhanced Representation of Time Series Which Allows Fast.. - Keogh, Pazzani (1998) (Correct)
Algorithms for Learning Augmented Bayesian Classifiers - Keogh, Pazzani (Correct)
Scaling up Dynamic Time Warping to Massive Datasets - Keogh, Pazzani (1999) (Correct)
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