Learning time-series shapelets
By Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme. Knowledge Discovery and Data Mining, 2014
491 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5900721a7ff9e91782dd4dc33956b09d3adb67f5
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
- Yixin Chen
- Martin Wistuba
- Zhicheng Cui
- Anthony J. Bagnall
- Jason Lines
- Nicolas Schilling
- Lars Schmidt-Thieme
- Aaron Bostrom
In Signal Processing
- Anthony J. Bagnall
- Jason Lines
- Aaron Bostrom
- Yixin Chen
- Zhicheng Cui
- Geoffrey I. Webb
- James Large
- Pierre-Alain Muller
Citations in the corpus
- Deep learning for time series classification: a review (2018, methodology): “This observation would also be backed up by the mathematical proof provided by Cui et al. (2016), that showed how the learned filters in a CNN can be considered a generic form of shapelets extracted by the learning shapelets algorithm (Grabocka et al., 2014).”
- Multi-Scale Convolutional Neural Networks for Time Series Classification (2016, methodology): “We also select 11 existing methods with state-of-the-art results published within the recent three years, including: DTW with a warping window constraint set through cross validation (DTW CV) [23], Fast Shapelet (FS) [24] , SAX with vector space model (SV) [29], Bag-of-SFA-Symbols (BOSS) [26], Shotg”
- Tslearn, A Machine Learning Toolkit for Time Series Data (2020, methodology): “Finally, keras (Chol-let et al., 2015) with tensorflow (Abadi et al., 2016) backend is an optional dependency that is necessary to use the shapelets module in tslearn that provides an efficient implementation of the shapelet model by Grabocka et al. (2014).”
- The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, methodology): “Grabocka et al. (2014) describe a shapelet discovery algorithm that adopts a heuristic gradient descent shapelet search procedure rather than enumeration.”
- Learning hyperparameter optimization initializations (2015, background): “We choose β = −100 as proposed in [22] such that ∑m i=1 xiσ (x)i is very close to the true minimum min {x1, .”