Results 11 to 20 of about 1,070,186 (234)

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships [PDF]

open access: yesSensors, 2022
This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series.
Johannes Stübinger, Dominik Walter
doaj   +3 more sources

Dynamic Dynamic Time Warping

open access: yesACM-SIAM Symposium on Discrete Algorithms, 2023
The Dynamic Time Warping (DTW) distance is a popular similarity measure for polygonal curves (i.e., sequences of points). It finds many theoretical and practical applications, especially for temporal data, and is known to be a robust, outlier-insensitive alternative to the \frechet distance. For static curves of at most $n$ points, the DTW distance can
Bringmann, Karl   +5 more
core   +4 more sources

Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package [PDF]

open access: greenJournal of Statistical Software, 2009
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.
Toni Giorgino
doaj   +3 more sources

DYNAMIC TIME WARPING FOR CROPS MAPPING [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available.
M. Belgiu   +3 more
doaj   +4 more sources

shapeDTW: Shape Dynamic Time Warping [PDF]

open access: bronzePattern Recognition, 2017
Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does
Jiaping Zhao, Laurent Itti
openalex   +5 more sources

Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping

open access: yesAtmosphere, 2022
Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns.
Fatin Nur Afiqah Suris   +4 more
doaj   +2 more sources

Dynamic Time Warping under limited warping path length [PDF]

open access: greenInformation Sciences, 2017
Dynamic Time Warping (DTW) is probably the most popular distance measure for time series data, because it captures flexible similarities under time distortions. However, DTW has long been suffering from the pathological alignment problem, and most existing solutions, which essentially impose rigid constraints on the warping path, are likely to miss the
Zheng Zhang   +5 more
openalex   +6 more sources

Feature trajectory dynamic time warping for clustering of speech segments [PDF]

open access: yesEURASIP Journal on Audio, Speech, and Music Processing, 2019
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories.
Lerato Lerato, Thomas Niesler
doaj   +4 more sources

Exact Mean Computation in Dynamic Time Warping Spaces [PDF]

open access: yesData Mining and Knowledge Discovery, 2018
Dynamic time warping constitutes a major tool for analyzing time series. In particular, computing a mean series of a given sample of series in dynamic time warping spaces (by minimizing the Fréchet function) is a challenging computational problem, so far solved by several heuristic and inexact strategies.
Markus Brill   +5 more
openaire   +5 more sources

On-Line Dynamic Time Warping for Streaming Time Series [PDF]

open access: green, 2017
Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning models for this particular kind of data. Nowadays, the proliferation of streaming data sources has ignited the interest and attention
Izaskun Oregi   +3 more
openalex   +5 more sources

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