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.

References in zbMATH (referenced in 21 articles , 1 standard article )

Showing results 1 to 20 of 21.
Sorted by year (citations)

1 2 next

  1. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Trimmed fuzzy clustering of financial time series based on dynamic time warping (2021)
  2. Itsaso Rodriguez, Itziar Irigoien, Basilio Sierra, Concepcion Arenas: dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns (2021) arXiv
  3. Maximilian Leodolter, Claudia Plant, Norbert Brändle: IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping (2021) not zbMATH
  4. Zhang, Chi; Fanaee-T, Hadi; Thoresen, Magne: Feature extraction from unequal length heterogeneous EHR time series via dynamic time warping and tensor decomposition (2021)
  5. Chainarong Amornbunchornvej: mFLICA: An R package for Inferring Leadership of Coordination From Time Series (2020) arXiv
  6. Chainarong Amornbunchornvej, Elena Zheleva, Tanya Berger-Wolf: Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis (2020) arXiv
  7. Modak, Soumita; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar: Unsupervised classification of eclipsing binary light curves through (k)-medoids clustering (2020)
  8. Owadally, Iqbal; Zhou, Feng; Otunba, Rasaq; Lin, Jessica; Wright, Douglas: Time series data mining with an application to the measurement of underwriting cycles (2019)
  9. Victor Maus and Gilberto Câmara and Marius Appel and Edzer Pebesma: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R (2019) not zbMATH
  10. Aslan, Sipan; Yozgatligil, Ceylan; Iyigun, Cem: Temporal clustering of time series via threshold autoregressive models: application to commodity prices (2018)
  11. 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)
  12. Debarsy, Nicolas; Dossougoin, Cyrille; Ertur, Cem; Gnabo, Jean-Yves: Measuring sovereign risk spillovers and assessing the role of transmission channels: a spatial econometrics approach (2018)
  13. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Robust fuzzy clustering of multivariate time trajectories (2018)
  14. Oke, Olufolajimi; Bhalla, Kavi; Love, David C.; Siddiqui, Sauleh: Spatial associations in global household bicycle ownership (2018)
  15. Schmitt, Eric; Tull, Christopher; Atwater, Patrick: Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives (2018)
  16. Philipp Boersch-Supan: rucrdtw: Fast time series subsequence search in R (2016) not zbMATH
  17. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  18. David Clifford; Glenn Stone: Variable Penalty Dynamic Time Warping Code for Aligning Mass Spectrometry Chromatograms in R (2012) not zbMATH
  19. Slaets, Leen; Claeskens, Gerda; Hubert, Mia: Phase and amplitude-based clustering for functional data (2012)
  20. de Gregorio, Alessandro; Iacus, Stefano Maria: Clustering of discretely observed diffusion processes (2010)

1 2 next