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Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.


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Proceedings Article
01 Jan 2001
TL;DR: 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.
Abstract: 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 sequences have the approximately the same overall component shapes, but these shapes do not line up in X-axis. Figure 1 shows this with a simple example. In order to find the similarity between such sequences, or as a preprocessing step before averaging them, we must "warp" the time axis of one (or both) sequences to achieve a better alignment. Dynamic time warping (DTW), is a technique for efficiently achieving this warping. In addition to data mining (Keogh & Pazzani 2000, Yi et. al. 1998, Berndt & Clifford 1994), DTW has been used in gesture recognition (Gavrila & Davis 1995), robotics (Schmill et. al 1999), speech processing (Rabiner & Juang 1993), manufacturing (Gollmer & Posten 1995) and medicine (Caiani et. al 1998).

1,131 citations

Book ChapterDOI
01 Jan 2002
TL;DR: Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis, but does not obey the triangular inequality and, thus, has resisted attempts at exact indexing.
Abstract: Publisher Summary The indexing of very large time series databases has attracted the attention of database community in recent years. The vast majority of work in this area has focused on indexing under the Euclidean distance metric. The problem of indexing time series has attracted much research interest in the database community. Most algorithms that are used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry, and finance. Unfortunately, however, DTW does not obey the triangular inequality and, thus, has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques, or abandoned the idea of indexing and concentrated on speeding up sequential search.

1,033 citations

Proceedings ArticleDOI
12 Aug 2012
TL;DR: 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.
Abstract: Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

969 citations

Posted Content
TL;DR: This paper presents the viability of MFCC to extract features and DTW to compare the test patterns and explains why the alignment is important to produce the better performance.
Abstract: — Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology The voice is a signal of infinite information A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques The non linear sequence alignment known as Dynamic Time Warping (DTW) introduced by Sakoe Chiba has been used as features matching techniques Since it’s obvious that the voice signal tends to have different temporal rate, the alignment is important to produce the better performanceThis paper present the viability of MFCC to extract features and DTW to compare the test patterns

846 citations

Journal ArticleDOI
TL;DR: The dtw package allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on.
Abstract: Dynamic time warping is a popular technique for comparing time series, providing both a distance measure that is insensitive to local compression and stretches and the warping which optimally deforms one of the two input series onto the other. A variety of algorithms and constraints have been discussed in the literature. The dtw package provides an unification of them; it allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on. The package also provides functions for visualizing alignments and constraints using several classic diagram types.

833 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377