R package dtw: Dynamic time warping algorithms. A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Aslan, Sipan; Yozgatligil, Ceylan; Iyigun, Cem: Temporal clustering of time series via threshold autoregressive models: application to commodity prices (2018)
- Beyaztas, Beste Hamiye; Beyaztas, Ufuk; Bandyopadhyay, Soutir; Huang, Wei-Min: New and fast block bootstrap-based prediction intervals for GARCH(1,1) process with application to exchange rates (2018)
- D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Robust fuzzy clustering of multivariate time trajectories (2018)
- Oke, Olufolajimi; Bhalla, Kavi; Love, David C.; Siddiqui, Sauleh: Spatial associations in global household bicycle ownership (2018)
- Zhao, Yanchang: R and data mining. Examples and case studies (2013)
- David Clifford; Glenn Stone: Variable Penalty Dynamic Time Warping Code for Aligning Mass Spectrometry Chromatograms in R (2012)
- Slaets, Leen; Claeskens, Gerda; Hubert, Mia: Phase and amplitude-based clustering for functional data (2012)
- de Gregorio, Alessandro; Iacus, Stefano Maria: Clustering of discretely observed diffusion processes (2010)
- Toni Giorgino: Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package (2009)