research-article

Learning time-series shapelets

Authors:

Josif Grabocka,

Nicolas Schilling,

Martin Wistuba,

Lars Schmidt-ThiemeAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 392 - 401

https://doi.org/10.1145/2623330.2623613

Published: 24 August 2014 Publication History

Abstract

Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.

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Cited By

Bahri OLi PFilali Boubrahimi SHamdi S(2025)Predicting Solar Energetic Particle Events with Time Series ShapeletsThe Astrophysical Journal10.3847/1538-4357/ada601980:1(128)Online publication date: 7-Feb-2025
https://doi.org/10.3847/1538-4357/ada601
Tang YXu ZHuang W(2025)Design of Self-Optimizing Polynomial Neural Networks with Temporal Feature Enhancement for Time Series ClassificationElectronics10.3390/electronics1403046514:3(465)Online publication date: 23-Jan-2025
https://doi.org/10.3390/electronics14030465
Wang YCai JYang HShi CZhang MWang JZhang RZhao X(2025)Interpretable deep classification of time series based on class discriminative prototype learningIntelligent Data Analysis: An International Journal10.1177/1088467X251319188Online publication date: 27-Feb-2025
https://doi.org/10.1177/1088467X251319188
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Index Terms

Learning time-series shapelets
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Bahri OLi PFilali Boubrahimi SHamdi S(2025)Predicting Solar Energetic Particle Events with Time Series ShapeletsThe Astrophysical Journal10.3847/1538-4357/ada601980:1(128)Online publication date: 7-Feb-2025
https://doi.org/10.3847/1538-4357/ada601
Tang YXu ZHuang W(2025)Design of Self-Optimizing Polynomial Neural Networks with Temporal Feature Enhancement for Time Series ClassificationElectronics10.3390/electronics1403046514:3(465)Online publication date: 23-Jan-2025
https://doi.org/10.3390/electronics14030465
Wang YCai JYang HShi CZhang MWang JZhang RZhao X(2025)Interpretable deep classification of time series based on class discriminative prototype learningIntelligent Data Analysis: An International Journal10.1177/1088467X251319188Online publication date: 27-Feb-2025
https://doi.org/10.1177/1088467X251319188
Song ZXu LHu XLiu YLiu WLi Q(2025)Research on a method of fault identification of rolling bearings based on time series shapeletsMeasurement and Control10.1177/00202940241245250Online publication date: 8-Jan-2025
https://doi.org/10.1177/00202940241245250
Jiao LWynne R(2025)Robust Identification of Vegetation Change Using Shapelet-Based Temporal Segmentation of Landsat Time-Series Stacks: A Case Study in the Qilian MountainsIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.352141718(4601-4618)Online publication date: 2025
https://doi.org/10.1109/JSTARS.2024.3521417
Jiazhen ZXiulai WShuxu Z(2025)A Fused Multiscale Pictorial Sequence Learning Mechanism Applied to Pavement DetectionIEEE Access10.1109/ACCESS.2025.352598813(7502-7511)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525988
Farahani MMcCormick MHarik RWuest T(2025)Time-series classification in smart manufacturing systemsRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10283991:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.rcim.2024.102839
Kathirgamanathan BNguyen TCaulfield BIfrim GCunningham P(2025)Explaining Fatigue in Runners Using Time Series Analysis on Wearable Sensor DataMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74633-8_6(87-103)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74633-8_6
Faouzi J(2024)Time Series Classification: A Review of Algorithms and ImplementationsTime Series Analysis - Recent Advances, New Perspectives and Applications10.5772/intechopen.1004810Online publication date: 25-Mar-2024
https://doi.org/10.5772/intechopen.1004810
Qu JQi CMeng H(2024)An Imbalanced Sequence Feature Extraction Approach for the Detection of LTE-R Cells with Degraded Communication PerformanceFuture Internet10.3390/fi1601003016:1(30)Online publication date: 16-Jan-2024
https://doi.org/10.3390/fi16010030
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