Eamonn J. Keogh

University of California - Riverside
https://www.cs.ucr.edu/~eamonn/
Publications353
h-index90
Citations40,994
Highly Influential Citations3,889
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A symbolic representation of time series, with implications for streaming algorithms
TLDR
A new symbolic representation of time series is introduced that is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
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Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
TLDR
This work introduces a new dimensionality reduction technique which it is called Piecewise Aggregate Approximation (PAA), and theoretically and empirically compare it to the other techniques and demonstrate its superiority.
View on Springer
Experiencing SAX: a novel symbolic representation of time series
TLDR
The utility of the new symbolic representation of time series formed is demonstrated, which allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
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Derivative Dynamic Time Warping
TLDR
Dynamic time warping (DTW), is a technique for efficiently achieving this warping of sequences that have the approximately the same overall component shapes, but these shapes do not line up in X-axis.
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Exact indexing of dynamic time warping
  • Eamonn J. Keogh
  • Computer Science
    Knowledge and Information Systems
  • 20 August 2002
TLDR
This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
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Exact Indexing of Dynamic Time Warping
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An online algorithm for segmenting time series
TLDR
This paper undertake the first extensive review and empirical comparison of all proposed techniques for mining time-series data with fatal flaws and introduces a novel algorithm that is empirically show to be superior to all others in the literature.
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Time series shapelets: a new primitive for data mining
TLDR
A new time series primitive, time series shapelets, is introduced, which can be interpretable, more accurate and significantly faster than state-of-the-art classifiers.
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Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping
TLDR
This work shows that by using a combination of four novel ideas the authors can search and mine truly massive time series for the first time, and shows that in large datasets they can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms.
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Querying and mining of time series data: experimental comparison of representations and distance measures
TLDR
An extensive set of time series experiments are conducted re-implementing 8 different representation methods and 9 similarity measures and their variants and testing their effectiveness on 38 time series data sets from a wide variety of application domains to provide a unified validation of some of the existing achievements.
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