Localized Random Shapelets

Guilleme, Mael; Malinowski, Simon; Tavenard, Romain; Renard, Xavier

doi:10.1007/978-3-030-39098-3_7

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The discovered shapelet prototypes are reported to be more general and expressive because they preserve the intrinsic shapes present in the data. In [92], Guilleme et al propose the method Localized Random Shapelets. This approach aims to generate more realistic and interpretable shapelets by adding shapelet localization to the traditional shapelet transform representation.…”

Section: Definition 8 (Multivariate Shapelet) Given a Tsc Datasetmentioning

confidence: 99%

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

et al. 2022

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Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions research directions regarding the type of explanations and the evaluation of explanations and interpretability.

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Section: Definition 8 (Multivariate Shapelet) Given a Tsc Datasetmentioning

confidence: 99%

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Many algorithms have been designed based on shapelets, some use discretization [16], or random selection [22,11] to filter candidate shapelets, others build shapelets location indicators from the data to generate a limited number of candidates [8]. Recent work also include shapelet localization as a feature, for evolutionary [21] or neural network [6] approaches, while others, similarly to our method, extract shapelets from different representations of the input data [12].…”

Section: Shapeletsmentioning

confidence: 99%

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

Guillaume,

Vrain,

Wael

2021

Preprint

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Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed, notably by methods using convolutional kernels, capable of reaching state-of-the-art performance while being highly scalable. We present a new formulation of time series shapelets including the notion of dilation, and a shapelet extraction method based on convolutional kernels, which is able to target the discriminant informations identified by convolutional kernels. Experiments performed on 108 datasets show that our method improves on the state-of-the-art for shapelet algorithms, and we show that it can be used to interpret results from convolutional kernels.Preprint. Under review.

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“…Wang et al [ 17 ] investigated adversarial regularization in order to enhance the interpretability of the discovered shapelets. Guillemé et al [ 18 ] investigated the added value of the location information of the discovered shapelets on top of distance-based information.…”

Section: Introductionmentioning

confidence: 99%

GENDIS: Genetic Discovery of Shapelets

Vandewiele

Ongenae

Turck

2021

Sensors

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In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages.

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Localized Random Shapelets

Cited by 6 publications

References 12 publications

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

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

GENDIS: Genetic Discovery of Shapelets

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