Team

David Schultz

David SchultzTelefon - David Schultz +49 30 - 314-74159
E-Mail - David Schultz E-Mail
 

Forschungsschwerpunkte

Time Series Analysis
Recurrence Plots
Signal Processing
Machine Learning

Aktuelle Projektbeteiligung

VW - Time Series Analysis
Recurrence Plot Research

Weitere Aktivitäten

Master Thesis: Signal Separation via Compressed Sensing

Publikationen

2018

Nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces
David Schultz, Brijnesh Johannes Jain
In: Pattern Recognition, Volume 74, Pages 340-358, 2018; 2018

Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces
Brijnesh Jain, David Schultz
In: Pattern Recognition, Volume 76, April 2018, Pages 349-366; 2018

Exact Mean Computation in Dynamic Time Warping Spaces
Markus Brill, Till Fluschnik, Vincent Froese,Brijnesh Jain, Rolf Niedermeier, David Schultz
In: SIAM International Conference on Data Mining (in press); 2018

2017

Optimal Warping Paths are unique for almost every pair of Time Series
Brijnesh Jain, David Schultz
In: arXiv preprint arXiv:1705.05681; 2017

2016

Approximate Recurrence Quantification Analysis (aRQA) in Code of Best Practice
Stephan Spiegel, David Schultz, Norbert Marwan
In: Recurrence Plots and Their Quantifications: Expanding Horizons: Proceedings of the 6th International Symposium on Recurrence Plots, Grenoble, France, 17-19 June 2015; 2016

A Reduction Theorem for the Sample Mean in Dynamic Time Warping Spaces
Brijnesh Jain, David Schultz
In: arXiv preprint arXiv:1610.04460; 2016

2015

Approximation of diagonal line based measures in recurrence quantification analysis
David Schultz, Stephan Spiegel, Norbert Marwan, Sahin Albayrak
In: Physics Letters A; 2015

2014

BestTime: Finding Representatives in Time Series Datasets
Stephan Spiegel, David Schultz, Sahin Albayrak
In: ECML-PKDD-14: Lecture Notes in Artificial Intelligence” (LNAI) Series, Springer; 2014