Using dynamic time warping distances as features for improved time series classification

By Rohit J. Kate. Data mining and knowledge discovery, 2016

338 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/446c53b37e3720d9b926d27de68aefa32ffa352d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. James Large
5. Yusuf Yaslan
6. Zehra Cataltepe
7. Hakan Gunduz
8. Eamonn J. Keogh

In Signal Processing

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. James Large
5. Eamonn J. Keogh
6. Rohit J. Kate
7. Yusuf Yaslan
8. Zehra Cataltepe

Citations in the corpus

• Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, background): “They only accept time series with the same length and they are sensitive to the time domain distortions [16].”
• The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, background): “2012); SAX vector space model (SAXVSM) (Senin and Malinchik 2013); bag of SFA symbols (BOSS) (Schäfer 2015); DTW features (Kate 2016); collective of transformation-based ensembles (COTE) (Bagnall et al.”