Results 11 to 20 of about 1,070,186 (234)
Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships [PDF]
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
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]
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]
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]
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
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]
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]
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]
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]
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