Weighted dynamic time warping for time series classification
By Youngseon Jeong, Myong Kee Jeong, Olufemi A. Omitaomu. Pattern Recognition, 2011
700 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/1ef345edfaff4e5f22c82efb01052de7f7f9fc9c
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
- Anthony J. Bagnall
- Jason Lines
- Charu C. Aggarwal
- Jon Hills
- Aaron Bostrom
- George C. Runger
- Eugene Tuv
- Mustafa Gokce Baydogan
In Signal Processing
- Anthony J. Bagnall
- Jason Lines
- Aaron Bostrom
- Jon Hills
- Charu C. Aggarwal
- James Large
- Eugene Tuv
- George C. Runger
Citations in the corpus
- Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “…derivative DTW with full window and window set through cross validation (DDTW and DDTWCV), weighted DTW (WDTW) and derivative weighted DTW (WDDTW) [8], longest common subsequence (LCSS), Edit Distance with Real Penalty (ERP) [29], Time Warp Edit (TWE) distance [9], and the Move-Split-Merge (MSM)…”
- Multi-layered gesture recognition with Kinect (2015, methodology): “Different from the method of evaluating the phase difference between the test and reference sequences (Jeong et al., 2011) and the method of assigning different weights to features (Reyes et al., 2011), we assign different weights to the frames of the reference gesture sequence.”
- The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, methodology): “The warping is controlled by a stiffness parameter, ν. Stiffness enforces a multiplicative penalty on the distance between matched points in a manner similar to WDTW.”
- Classification of time series by shapelet transformation (2013, methodology): “We experiment with measures based on the analysis of variance test for differences in means, and the non-parametric Kruskal-Wallis and Mood’s median tests for difference in medians. We conduct our experiments on 29 datasets, 17 from the UCR repository and 12 of we provide ourselves. This research is”
- Outlier Analysis (2013, methodology): “In the fully supervised scenario, examples of both the normal data and the anomalous shapes are available [141, 237, 343, 474, 490].”
- A shapelet transform for time series classification (2012, background): “Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications Data Mining Keywords time series, shapelet, .lter, transformation 1.”
- A time series forest for classification and feature extraction (2013, neutral): “[12, 21, 8, 24].”
- Classification of time-series images using deep convolutional neural networks (2017, background): “4,5 8 Figure 1: From time-series signal to recurrence plot.”
- A Bag-of-Features Framework to Classify Time Series (2013, methodology): “For time series, one nearest neighbor (NN) classifiers with euclidean (NNEuclidean), or a dynamic time warping distance (NNDTW) have been widely and successfully used [1], [2], [3], [4], [5].”
- A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “However, when there is a need for a more complete description, other approaches are convenient: dynamic time warping [72], hidden Markov models [6] or neural networks [47], followed by an action recognition step and semantic description phase.”