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473
SparseDTW: A Novel Approach to Speed up Dynamic Time Warping
, 2012
"... We present a new spaceefficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behi ..."
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Cited by 7 (1 self)
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We present a new spaceefficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea
Derivative dynamic time warping
 In SIAM International Conference on Data Mining
, 2001
"... Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two se ..."
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Cited by 121 (1 self)
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two
Scaling up Dynamic Time Warping for Datamining Applications
 In Proc. 6th Int. Conf. on Knowledge Discovery and Data Mining
, 2000
"... There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle dist ..."
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Cited by 84 (3 self)
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distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate
Scaling up Dynamic Time Warping to Massive Datasets
, 1999
"... There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extre ..."
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Cited by 70 (1 self)
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be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular
Indexing multidimensional timeseries with support for multiple distance measures
, 2003
"... Although most timeseries data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of ..."
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Cited by 121 (15 self)
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, that offers enhanced robustness, particularly for noisy data, which are encountered very often in real world applications. However, our index is able to accommodate other distance measures as well, including the ubiquitous Euclidean distance, and the increasingly popular Dynamic Time Warping (DTW). While
Stream Monitoring under the Time Warping Distance
"... Data stream processing has recently attracted an increasing amount of interest. The goal of this paper is to monitor numerical streams, and to find subsequences that are similar to a given query sequence, under the DTW (Dynamic Time Warping) distance. Applications include word spotting, sensor patte ..."
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Cited by 30 (3 self)
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Data stream processing has recently attracted an increasing amount of interest. The goal of this paper is to monitor numerical streams, and to find subsequences that are similar to a given query sequence, under the DTW (Dynamic Time Warping) distance. Applications include word spotting, sensor
Qualitative approximation to Dynamic Time Warping similarity between time series data
"... Dynamic time warping (DTW) is a method for calculating the similarity between two time series which can occur at different times or speeds. Although its effectiveness made it very popular in several disciplines, its time complexity of O(N2) makes it useful only for relatively short time series. In ..."
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Cited by 1 (0 self)
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Dynamic time warping (DTW) is a method for calculating the similarity between two time series which can occur at different times or speeds. Although its effectiveness made it very popular in several disciplines, its time complexity of O(N2) makes it useful only for relatively short time series
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
, 2008
"... Abstract In a way similar to the stringtostring correction problem we address time series similarity in light of a timeseriestotimeseriescorrection problem for which the similarity between two time series is measured as the minimum cost sequence of "edit operations " needed to tran ..."
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to transform one time series into another. To define the “edit operations ” we use the paradigm of a graphical editing process and end up with a dynamic programming algorithm that we call Time Warp Edit Distance (TWED). TWED is slightly different in form from Dynamic Time Warping, Longest Common Subsequence
Weighted dynamic time warping for time series classification,”
 Pattern Recognition,
, 2011
"... a b s t r a c t Dynamic time warping (DTW), which finds the minimum path by providing nonlinear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phas ..."
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Cited by 9 (0 self)
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a b s t r a c t Dynamic time warping (DTW), which finds the minimum path by providing nonlinear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding
Three Myths about Dynamic Time Warping Data
 Mining, in the Proceedings of SIAM International Conference on Data Mining (2005
"... The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for va ..."
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Cited by 43 (14 self)
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The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility
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