Accelerating the discovery of unsupervised-shapelets

Zakaria, Jesin; Mueen, Abdullah; Keogh, Eamonn; Young, Neal E.

doi:10.1007/s10618-015-0411-4

Cited by 24 publications

(6 citation statements)

References 33 publications

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“…It is therefore an exhaustive search, as were the early supervised approaches. Ushapelets have been used in several works since their initial introduction [24,33]. Since these unsupervised methods take a similar approach to the original supervised shapelets, they have the same drawbacks.…”

Section: Unsupervised Shapeletsmentioning

confidence: 99%

CDPS: Constrained DTW-Preserving Shapelets

Amouri

Lampert

Gançarski

et al. 2023

Lecture Notes in Computer Science

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The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, differences in sample duration, and/or compression and dilation of a signal means that Euclidean distance is unsuitable in many cases. As such, several similarity measures specific to time-series have been proposed, Dynamic Time Warping (DTW) being the most popular. Nevertheless, DTW does not respect the axioms of a metric and therefore Learning DTW-Preserving Shapelets (LDPS) have been developed to regain these properties by using the concept of shapelet transform. LDPS learns an unsupervised representation that models DTW distances using Euclidean distance in shapelet space. This article proposes constrained DTW-preserving shapelets (CDPS), in which a limited amount of user knowledge is available in the form of must link and cannot link constraints, to guide the representation such that it better captures the user's interpretation of the data rather than the algorithm's bias. Subsequently, any unconstrained algorithm can be applied, e.g. K-means clustering, k-NN classification, etc, to obtain a result that fulfils the constraints (without explicit knowledge of them). Furthermore, this representation is generalisable to out-of-sample data, overcoming the limitations of standard transductive constrainedclustering algorithms. CLDPS is shown to outperform the state-of-theart constrained-clustering algorithms on multiple time-series datasets. An open-source implementation based on PyTorch is available 1 , which takes full advantage of GPU acceleration.

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Section: Unsupervised Shapeletsmentioning

confidence: 99%

CDPS: Constrained DTW-Preserving Shapelets

Amouri

Lampert

Gançarski

et al. 2023

Lecture Notes in Computer Science

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“…The feature-based method transforms time series into feature vectors, relying on extracting features from original MTS data, then constructing models on time features and classifying them by traditional classifiers. Typical feature-based methods are two-dimensional singular value decomposition (2dSVD) [19], unsupervised locality preserving projections (LPP) [20], symbolic representation for MTS (SMTS) [21], Shapelets [22] and its various variants [23]. 2dSVD captures eigenvectors of covariance matrices of MTS data as features, and calculates the distance between two MTSs by measuring the distance of these features.…”

Section: Related Workmentioning

confidence: 99%

Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification

Wu¹,

Zhang²,

He³

et al. 2019

IEEE Access

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The multivariate time series (MTS) classification is an important classification problem in which data has the temporal attribute. Because relationships between many variables of the MTS are complex and time-varying, existing methods perform not well in MTS classification with many attribute variables. Thus, in this paper, we propose a novel model-based classification method, called Kullback-Leibler Divergence-based Gaussian Model Classification (KLD-GMC), which converts the original MTS data into two important parameters of the multivariate Gaussian model: the mean vector and the inverse covariance matrix. The inverse covariance is the most important parameter, which can obtain the information between the variables. So that the more variables, the more information could be obtained by the inverse covariance, KLD-GMC can deal with the relationship between variables well in the MTS. Then the sparse inverse covariance of each subsequence is solved by Graphical Lasso. Furthermore, the Kullback-Leibler divergence is used as the similarity measurement to implement the classification of unlabeled subsequences, because it can effectively measure the similarity between different distributions. Experimental results on classical MTS datasets demonstrate that our method can improve the performance of multivariate time series classification and outperform the state-of-the-art methods.

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“…It is useful for exploratory data mining and often used as inputs for classification of time series, clustering, segmentation (Bagnall et al, 2017;Mori et al, 2017;Dau et al, 2016;Petitjean et al, 2014). Time series motif analysis has been widely used in diverse fields (Yeh et al, 2018;Zhu et al, 2018;Linardi et al, 2018a,b;Yeh et al, 2017;Zakaria et al, 2016). Gomes and Batista presented a SAX-based motif discovery method to classify the urban sound (Gomes and Batista, 2015).…”

Section: Introductionmentioning

confidence: 99%

Time series classification based on triadic time series motifs

Xie

Han

Zhou

2019

Int. J. Mod. Phys. B

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It is of great significance to identify the characteristics of time series to qualify their similarity. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map. Based on the similarity of motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We further apply the motif analysis method to the UCR Time Series Classification Archive and provide evidence of good classification ability for some data sets. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain data sets investigated in this work.

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Accelerating the discovery of unsupervised-shapelets

Cited by 24 publications

References 33 publications

CDPS: Constrained DTW-Preserving Shapelets

CDPS: Constrained DTW-Preserving Shapelets

Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification

Time series classification based on triadic time series motifs

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