Derivative Dynamic Time Warping

By Eamonn J. Keogh, Michael J. Pazzani. SDM, 2001

1,289 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9c211c20370cb09f1dd6ad2d15219ed38a7a5859

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In Signal Processing

Citations in the corpus

Gesture recognition for human-robot collaboration: A review (2017, background): “Every classi ﬁ cation algorithm has own advantages and disadvantages.”
End-To-End Speech Synthesis Applied to Brazilian Portuguese (2020, background): “It may be difﬁcult to compare two audios with the same sentence, as they may not align perfectly due to the sentence starting at different times in each audio and/or having different rates [Keogh and Pazzani, 2001].”
Preprocessing techniques for context recognition from accelerometer data (2010, background): “More recently, researchers have also developed the derivative DTW (DDTW) [25] and used it to detect activities such as walking, going up and down flights of stairs [39].”
InceptionTime: Finding AlexNet for time series classification (2019, background): “Much research has subsequently focused on finding alternative similarity measures (Marteau 2009; Stefan et al. 2013; Keogh and Pazzani 2001; Vlachos et al. 2006), however none have been found to significantly outperform NN-DTW on the UCR Archive (Lines and Bagnall 2015).”
Vision based hand gesture recognition for human computer interaction: a survey (2012, methodology): “Examples are applying constraints to DTW (Eamonn and Pazzani 2001), approximation of the algorithm (Stan and Philip 2004) and lower bounding techniques (Keogh and Ratanamahatana 2005).”
Weighted dynamic time warping for time series classification (2011, methodology): “In the area of new distance measures for time series classification and clustering, Keogh and Pazzani [11] proposed a modification of DTW, called Derivative Dynamic Time Warping (DDTW), which transforms an original sequence into a higher level feature of shape by estimating derivatives.”
Why we search: visualizing and predicting user behavior (2007, methodology): “We believe that even if different alignment techniques [8][9] are used, this visualization is still informative.”
A review on time series data mining (2011, methodology): “An ensemble-index, is proposed by Keogh et al. (2001b) and Vlachos et al. (2006), which ensembles two or more representations for indexing.”
k-Nearest Neighbour Classifiers - A Tutorial (2020, background): “• Dynamic Time Warping (DTW): Because two time series may be fundamentally similar but offset or slightly distorted, DTW allows the time axis to be warped to identify underlying similarities [25].”
Dude, where's my card?: RFID positioning that works with multipath and non-line of sight (2013, methodology): “To address these potential variations in feature values, we leverage a simple modification [25] of DTW.”
FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space (2004, background): “In addition to s peech recognition, dynamic time warping has also been fou nd seful in many other disciplines [8], including data mining, esture recognition, robotics, manufacturing, and medicine.”
Time series classification with ensembles of elastic distance measures (2015, methodology): “…areas in b, but it does not strictly prevent traversal of those areas as a warping window does 2.3 Derivative Dynamic Time Warping Keogh and Pazzani proposed a modification of DTW called DDTW (Keogh and Pazzani 2011) that first transforms the series into a series of first order differences.”