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Dynamic time warping Déformation temporelle dynamique 동적 시간 워핑 Dynamic-Time-Warping Алгоритм динамической трансформации временной шкалы Deformación dinámica del tiempo Dynamic time warping Dynamic time warping
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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 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. 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. 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. Dynamische Zeitnormierung (engl. dynamic time warping) bezeichnet einen Algorithmus, der Wertefolgen unterschiedlicher Länge aufeinander abbildet. 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. 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. 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
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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. DTW pode ser utilizada para alinhar qualquer tipo de dado que obedeça uma ordem temporal, como vídeo, áudio e imagens. Entre as diversas aplicações da DTW, encontra-se o reconhecimento de fala e de assinatura, bem como o alinhamento de gravações musicais com suas respectivas partituras. Apesar do algoritmo que calcula a DTW fornecer um valor numérico relacionado a uma medida de distância, ele deve ser utilizado com cautela. Por não obedecer a desigualdade triangular, a DTW não pode ser considerada uma métrica. Consequentemente, métodos de indexação em espaço métrico não são aplicáveis. 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. In generale, DTW è un metodo che permette di trovare una corrispondenza ottima tra due sequenze, attraverso una distorsione non lineare rispetto alla variabile indipendente (tipicamente il tempo). Alcune restrizioni per il calcolo della corrispondenza sono generalmente utilizzate: deve essere garantita la monotonicità nelle corrispondenze, ed il limite massimo di possibili corrispondenze tra elementi contigui della sequenza. 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. Временные ряды — широко распространенный тип данных[уточнить], встречающийся, фактически, в любой научной области, и сравнение двух последовательностей является стандартной задачей. Для вычисления отклонения бывает достаточно простого измерения расстояния между компонентами двух последовательностей (евклидово расстояние). Однако часто две последовательности имеют приблизительно одинаковые общие формы, но эти формы не выровнены по оси X. Чтобы определить подобие между такими последовательностями, мы должны «деформировать» ось времени одной (или обеих) последовательности, чтобы достигнуть лучшего выравнивания. 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. L'algorithme DTW a été exploité en vidéo, audio, graphique par ordinateur, bio-informatique... et peut être appliqué dans toute situation où les données peuvent être transformées en une représentation linéaire. Une application célèbre est l'application en reconnaissance automatique de la parole, où il est nécessaire de tenir compte de vitesses d'élocution très variables. De façon générale, DTW est une méthode qui recherche un appariement optimal entre deux séries temporelles, sous certaines restrictions. Les séries temporelles sont déformées par transformation non linéaire de la variable temporelle, pour déterminer une mesure de leur similarité, indépendamment de certaines transformations non linéaires du temps. Cette méthode d'alignement de séries temporelles est souvent utilisée dans le contexte de modèles de Markov cachés. * Portail de l'informatique théorique 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. Dynamische Zeitnormierung (engl. dynamic time warping) bezeichnet einen Algorithmus, der Wertefolgen unterschiedlicher Länge aufeinander abbildet. 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 distancias entre todos los miembros de una relación, como manera de calcular la distancia óptima entre los miembros de un grupo.​ 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. In general, DTW is a method that calculates an optimal match between two given sequences (e.g. time series) with certain restriction and rules: * Every index from the first sequence must be matched with one or more indices from the other sequence, and vice versa * The first index from the first sequence must be matched with the first index from the other sequence (but it does not have to be its only match) * The last index from the first sequence must be matched with the last index from the other sequence (but it does not have to be its only match) * The mapping of the indices from the first sequence to indices from the other sequence must be monotonically increasing, and vice versa, i.e. if are indices from the first sequence, then there must not be two indices in the other sequence, such that index is matched with index and index is matched with index , and vice versa The optimal match is denoted by the match that satisfies all the restrictions and the rules and that has the minimal cost, where the cost is computed as the sum of absolute differences, for each matched pair of indices, between their values. The sequences are "warped" non-linearly in the time dimension to determine a measure of their similarity independent of certain non-linear variations in the time dimension. This sequence alignment method is often used in time series classification. Although DTW measures a distance-like quantity between two given sequences, it doesn't guarantee the triangle inequality to hold. In addition to a similarity measure between the two sequences, a so called "warping path" is produced, by warping according to this path the two signals may be aligned in time. The signal with an original set of points X(original), Y(original) is transformed to X(warped), Y(warped). This finds applications in genetic sequence and audio synchronisation. In a related technique sequences of varying speed may be averaged using this technique see the section. This is conceptually very similar to the Needleman–Wunsch algorithm.
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