A shapelet transform for time series classification

By Jason Lines, L. M. Davis, Jon Hills, Anthony J. Bagnall. Knowledge Discovery and Data Mining, 2012

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/27c6ec2081934a62a2adc1e06c496fc95bfd4de1

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. Yixin Chen
2. Zhicheng Cui
3. Anthony J. Bagnall
4. Jason Lines
5. Houtao Deng
6. Martin Wistuba
7. George C. Runger
8. Jon Hills

In Economics and Econometrics

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. Jon Hills
5. Zhicheng Cui
6. Yixin Chen
7. Eamonn J. Keogh
8. Lars Schmidt-Thieme

In Signal Processing

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. Yixin Chen
5. Zhicheng Cui
6. Jon Hills
7. Eugene Tuv
8. George C. Runger

Citations in the corpus

• Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “46 of these are available from the UCR repository [1], 24 were used in other published work [6], [11], [12], [23] and two are new datasets we present for the first time.”
• A time series forest for classification and feature extraction (2013, background): “[10] have successfully shown that time series shapelets can produce highly interpretable results.”
• Learning time-series shapelets (2014, methodology): “Moreover, discovering shapelets has been a hot topic in the time-series domain during the last five years [17, 11, 10, 19, 8, 12, 9].”
• Multi-Scale Convolutional Neural Networks for Time Series Classification (2016, background): “A more recent study [18] constructs the feature vector such that the value of each feature is the minimum distance between anywhere in the time series and the corresponding shapelet.”
• Exploiting multi-channels deep convolutional neural networks for multivariate time series classification (2015, background): “While some of other researchers tried to reduce the computation of 1-NN by constructing data dictionary [10, 15, 17, 37].”
• Classification of time series by shapelet transformation (2013, methodology): “This research is an extension of the work presented at two conferences Lines and Bagnall (2012), Lines et al. (2012).”
• Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks (2014, background): “While another part of researchers tried to reduce the computation of 1-NN by constructing data dictionary [12, 7, 14, 22].”
• Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets (2013, background): “The utility of shapelets has been confirmed and extended by many independent groups of researchers [1][6][9][10][13][15][16][29][30].”