About: Dynamic time warping

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Dynamic time warping Goto Sponge NotDistinct Permalink

An Entity of Type : yago:WikicatAlgorithms, within Data Space : dbpedia.org associated with source document(s)
QRcode icon

http://dbpedia.org/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FDynamic_time_warping

In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a linear sequence can be analyzed with DTW. A well-known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial shape matching applications.

Attributes	Values
rdf:type	software yago:Abstraction100002137 yago:Act100030358 yago:Activity100407535 yago:Algorithm105847438 yago:Event100029378 yago:Procedure101023820 yago:PsychologicalFeature100023100 yago:YagoPermanentlyLocatedEntity yago:Rule105846932 yago:WikicatAlgorithms
rdfs:label	Dynamic-Time-Warping (de) Deformación dinámica del tiempo (es) Dynamic time warping (en) Déformation temporelle dynamique (fr) Dynamic time warping (it) 동적 시간 워핑 (ko) Dynamic time warping (pt) Алгоритм динамической трансформации временной шкалы (ru)
rdfs:comment	Dynamische Zeitnormierung (engl. dynamic time warping) bezeichnet einen Algorithmus, der Wertefolgen unterschiedlicher Länge aufeinander abbildet. (de) 시간 관련 분석에서 dynamic time warping(DTW), 즉 동적 시간 워핑은 얼추 비슷한 두개의 다른 속도의 시간축의 파장의 유사성을 측정하는 알고리즘이다. 예를 들어 보행의 유사성같은 것들또한 DTW를 통해 검출 될 수 있다. DTW는 그래픽, 오디오,비디오 등에서 많이 사용되어왔다. 가장 유명한 응용사례로는 자동 음성 인식기능이 있다.일반적으로 DTW는 2개의 주어진 시퀀스 사이의 최적 매칭을 계산하는 방법이다. 이러한 시퀀스들은 시간 차원에서 비선형적으로 워프되어 유사성을 판별한다. 이러한 시퀀스 정렬법은 시간 계열 분류에도 사용되곤한다. 두 시퀀스 사이의 유사성을 측정하기 위해서 warping path 라는 것이 만들어졌는데, 이 경로를 따르는 워핑으로 시간을 나열한다. 오리지널 X와 오리지널 Y로 시작하는 이 신호는 warped X와 오리지널 Y로 되게 될 것이다.그리하여 두 개의 시퀀스를 싱크로를 맞춘다. (ko) In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a linear sequence can be analyzed with DTW. A well-known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial shape matching applications. (en) En análisis de series temporales, la deformación dinámica del tiempo (en inglés, Dynamic Time Warping, DTW) es un algoritmo para medir la similitud entre dos secuencias temporales que permite obtener un buen ajuste incluso frente a un desfase en la velocidad o en el tiempo. Se trata de un algoritmo de aprendizaje no supervisado, puesto que no necesita ayuda externa para realizar inferencias sobre los datos, aunque puede combinarse con otros métodos para realizar aprendizaje supervisado. Aunque el nombre implica series temporales, puede usarse para todo tipo de datos, como reconocimiento facial, firmas biométricas e incluso clasificación de señales genómicas. Conceptualmente, es similar al algoritmo Needleman-Wunsch, en tanto a que ambos realizan una matriz de disimilitud, con las dista (es) La déformation temporelle dynamique (algorithme DTW pour Dynamic Time Warping en anglais) est un algorithme permettant de mesurer la similarité entre deux suites qui peuvent varier au cours du temps. Par exemple des similarités entre des pas dans des vidéos peuvent être détectées même si dans l'une ou l'autre des vidéos le sujet a marché plus rapidement ou plus lentement, ou encore si au cours de l'une ou l'autre le sujet a accéléré ou ralenti. * Portail de l'informatique théorique (fr) Il Dynamic time warping, o DTW, è un algoritmo che permette l'allineamento tra due sequenze, e che può portare ad una misura di distanza tra le due sequenze allineate.Tale algoritmo è particolarmente utile per trattare sequenze in cui singole componenti hanno caratteristiche che variano nel tempo, e per le quali la semplice espansione o compressione lineare delle due sequenze non porta risultati soddisfacenti. È stato utilizzato in diversi campi di applicazione, dal Riconoscimento vocale, al riconoscimento di attività motorie. (it) Dynamic time warping (DTW) é um algoritmo para comparar e alinhar duas séries temporais. A DTW é utilizada para encontrar o alinhamento não-linear ótimo entre duas sequências de valores numéricos. Dessa maneira, é possível encontrar padrões entre medições de eventos com diferentes ritmos. Por exemplo, é possível casar a série temporal obtida por acelerômetros (ou outros sensores) de duas pessoas andando em diferentes velocidades. (pt) Алгоритм динамической трансформации временно́й шкалы (DTW-алгоритм, от англ. dynamic time warping) — алгоритм, позволяющий найти оптимальное соответствие между временными последовательностями. Впервые применен в распознавании речи, где использован для определения того, как два речевых сигнала представляют одну и ту же исходную произнесённую фразу. Впоследствии были найдены применения и в других областях. (ru)
foaf:depiction
dcterms:subject	Multivariate time series Dynamic programming Articles with example pseudocode Machine learning algorithms
Wikipage page ID	342855 (xsd:integer)
Wikipage revision ID	1122716606 (xsd:integer)
Link from a Wikipage to another Wikipage	Multivariate time series Power analysis Elastic matching Time series Algorithm Dynamic programming Sequence alignment Levenshtein distance Functional data analysis Mlpy Optimal matching Fréchet distance Viterbi algorithm Articles with example pseudocode Machine learning algorithms Time Warp Edit Distance Triangle inequality Acceleration Dynamic programming Diffeomorphism Hirschberg's algorithm Speaker recognition Speech recognition K-nearest neighbors algorithm Hidden Markov model Needleman–Wunsch algorithm Time series analysis Radial basis function Multiple sequence alignment Shape analysis (digital geometry) Non-linear Wagner–Fischer algorithm Nonlinear mixed-effects model Signature recognition Multiple alignment Strong exponential time hypothesis
Link from a Wikipage to an external page	http://biointelligence.hu/pyhubs/ http://java-ml.sourceforge.net/ https://csdl.ics.hawaii.edu/techreports/2008/08-04/08-04.pdf https://dynamictimewarping.github.io/ https://gravitino.github.io/cudadtw/ https://mvnrepository.com/artifact/com.github.davidmoten/fastdtw https://tslearn.readthedocs.io/en/latest/%23 https://github.com/ChangWeiTan/UltraFastWWS https://github.com/MonashTS/tempo https://github.com/baggepinnen/DynamicAxisWarping.jl https://github.com/cesarsotovalero/time-series-classification https://github.com/doblak/ndtw https://github.com/fpetitjean/DBA https://github.com/garrettwrong/cuTWED https://github.com/hfink/matchbox https://github.com/kirel/sketch-a-char https://github.com/lemire/lbimproved https://github.com/nickgillian/grt/wiki https://github.com/rmaestre/FastDTW https://github.com/talcs/simpledtw https://pypi.python.org/pypi/pydtw https://archive.org/details/fundamentalsofsp00rabi https://sourceforge.net/p/java-ml/java-ml-code/ci/9f6726deab4e55b7617478bc51e29c20308bffb9/tree/net/sf/javaml/distance/dtw/FastDTW.java https://www.springer.com/cda/content/document/cda_downloaddocument/9783540740476-1.pdf%3FSGWID=0-0-45-452103-p173751818 https://cran.r-project.org/package=dtw https://pypi.org/project/dtw-python/
sameAs	Dynamic time warping

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (61 GB total memory, 49 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software