Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets

  title={Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets},
  author={Antoine Guillaume and Christel Vrain and Wael Elloumi},
. Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. We present a new formulation of time series shapelets including the notion of dilation, and we introduce a new shapelet feature to enhance their discriminative power for classification. Experiments performed on 112 datasets show that our method improves on the state-of-the-art shapelet algorithm, and achieves… 



Learning time-series shapelets

A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied and can learn true top-K shapelets by capturing their interaction.

Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets

This work proposes a fast shapelet discovery algorithm that outperforms the current state-of-the-art by two or three orders of magnitude, while producing models with accuracy that is not perceptibly different.

Binary Shapelet Transform for Multiclass Time Series Classification

A one vs all encoding scheme is evaluated which simplifies the quality assessment calculations, speeds up the execution through facilitating more frequent early abandon and increases accuracy for multi-class problems.

GENDIS: Genetic Discovery of Shapelets

A new paradigm for shapelet discovery is proposed, which is based on evolutionary computation and is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives.

Efficient Learning of Timeseries Shapelets

The shapelet discovery task is reformulated as a numerical optimization problem and the shapelet positions are learned by combining the generalized eigenvector method and fused lasso regularizer to encourage a sparse and blocky solution.

Ultra-Fast Shapelets for Time Series Classification

It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes.

Localized Random Shapelets

This paper designs an interpretable shapelet model that takes into account the localization of the shapelets in the time series and designs a hierarchical feature selection process using regularization that has competitive performance compared to state-of-the-art shapelet-based classifiers, while providing better interpretability.

Time series shapelets: a new primitive for data mining

A new time series primitive, time series shapelets, is introduced, which can be interpretable, more accurate and significantly faster than state-of-the-art classifiers.

A shapelet transform for time series classification

This work describes a means of extracting the k best shapelets from a data set in a single pass, and then uses these shapelets to transform data by calculating the distances from a series to each shapelet.

ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

This paper shows that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods for time series classification.