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Data Mining and Knowledge Discovery

Erschienen in:

13.07.2020

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

verfasst von: Angus Dempster, François Petitjean, Geoffrey I. Webb

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 5/2020

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Abstract

Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods. Using this method, it is possible to train and test a classifier on all 85 ‘bake off’ datasets in the UCR archive in \(<\,2\,\hbox {h}\), and it is possible to train a classifier on a large dataset of more than one million time series in approximately 1 h.

Vorheriger Artikel Large-scale network motif analysis using compression

Nächster Artikel Active learning for hierarchical multi-label classification

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Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31(3):606–660MathSciNetCrossRef

Bagnall A, Lines J, Vickers W, Keogh E (2019) The UEA & UCR time series classification repository. http://www.timeseriesclassification.com

Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271

Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? J Mach Learn Res 17(5):1–10MathSciNetMATH

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828CrossRef

Bostrom A, Bagnall A (2015) Binary shapelet transform for multiclass time series classification. In: Madria S, Hara T (eds) Big data analytics and knowledge discovery. Springer, Cham, pp 257–269CrossRef

Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60(2):223–311MathSciNetCrossRef

Boureau YL, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Fürnkranz J, Joachims T (eds) Proceedings of the 27th international conference on machine learning, Omnipress, USA, pp 111–118

Cox D, Pinto N (2011) Beyond simple features: a large-scale feature search approach to unconstrained face recognition. Face Gesture 2011:8–15

Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. arXiv:1603.06995

Dau HA, Bagnall A, Kamgar K, Yeh CCM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2019) The UCR time series archive. J Autom Sinica 6(6):1293–1305CrossRef

Dau HA, Keogh E, Kamgar K et al (2018) UCR time series classification archive (briefing document). https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH

Dongarra J, Gates M, Haidar A, Kurzak J, Luszczek P, Tomov S, Yamazaki I (2018) The singular value decomposition: anatomy of optimizing an algorithm for extreme scale. SIAM Rev 60(4):808–865MathSciNetCrossRef

Farahmand A, Pourazarm S, Nikovski D (2017) Random projection filter bank for time series data. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30. MIT Press, Cambridge, pp 6562–6572

Franceschi J, Dieuleveut A, Jaggi M (2019) Unsupervised scalable representation learning for multivariate time series. In: Seventh international conference on learning representations, learning from limited labeled data workshop

García S, Herrera F (2008) An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694MATH

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, CambridgeMATH

Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Disc 28(4):851–881MathSciNetCrossRef

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P (2019a) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963MathSciNetCrossRef

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P (2019b) Deep neural network ensembles for time series classification. In: International joint conference on neural networks, pp 1–6

Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, Idoumghar L, Muller P, Petitjean F (2019c) InceptionTime: finding AlexNet for time series classification. arXiv:1909.04939

Jarrett K, Kavukcuoglu K, Ranzato M, LeCun Y (2009) What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th international conference on computer vision, pp 2146–2153

Jimenez A, Raj B (2019) Time signal classification using random convolutional features. In: 2019 IEEE international conference on acoustics, speech and signal processing

Karlsson I, Papapetrou P, Boström H (2016) Generalized random shapelet forests. Data Min Knowl Disc 30(5):1053–1085MathSciNetCrossRef

Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Third international conference on learning representations. arXiv:1412.6980

Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates Inc, Red Hook, pp 1097–1105

Lam SK, Pitrou A, Seibert S (2015) Numba: a LLVM-based python JIT compiler. In: Proceedings of the second workshop on the LLVM compiler infrastructure in HPC, pp 1–6

Le Nguyen T, Gsponer S, Ilie I, O’Reillly M, Ifrim G (2019) Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations. Data Min Knowl Disc 33(4):1183–1222MathSciNetCrossRef

Lin M, Chen Q, Yan S (2014) Network in network. In: Second international conference on learning representations. arXiv:1312.4400

Lines J, Taylor S, Bagnall A (2018) Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles. ACM Trans Knowl Discov Data 12(5):52:1–52:35CrossRef

Lubba CH, Sethi SS, Knaute P, Schultz SR, Fulcher BD, Jones NS (2019) catch22: CAnonical Time-series CHaracteristics. Data Min Knowl Disc 33(6):1821–1852CrossRef

Lucas B, Shifaz A, Pelletier C, O’Neill L, Zaidi N, Goethals B, Petitjean F, Webb GI (2019) Proximity forest: an effective and scalable distance-based classifier for time series. Data Min Knowl Disc 33(3):607–635CrossRef

Middlehurst M, Vickers W, Bagnall A (2019) Scalable dictionary classifiers for time series classification. In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R (eds) Intelligent data engineering and automated learning. Springer, Cham, pp 11–19

Morrow A, Shankar V, Petersohn D, Joseph A, Recht B, Yosef N (2016) Convolutional kitchen sinks for transcription factor binding site prediction. In: NIPS workshop on machine learning in computational biology

Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free? Weakly-supervised learning with convolutional neural networks. In: 2015 IEEE conference on computer vision and pattern recognition, pp 685–694

Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. In: NIPS autodiff workshop

Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MathSciNetMATH

Petitjean F, Inglada J, Gancarski P (2012) Satellite image time series analysis under time warping. IEEE Trans Geosci Remote Sens 50(8):3081–3095CrossRef

Pinto N, Doukhan D, DiCarlo JJ, Cox DD (2009) A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Comput Biol 5(11):1–12MathSciNetCrossRef

Rahimi A, Recht B (2008) Random features for large-scale kernel machines. In: Platt JC, Koller D, Singer Y, Roweis ST (eds) Advances in neural information processing systems, vol 20. Curran Associates Inc, Red Hook, pp 1177–1184

Rahimi A, Recht B (2009) Weighted sums of random kitchen sinks: replacing minimization with randomization in learning. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems, vol 21. MIT Press, Cambridge, pp 1313–1320

Raza A, Kramer S (2019) Accelerating pattern-based time series classification: a linear time and space string mining approach. Knowl Inf Syst 62:1113–1141CrossRef

Renard X, Rifqi M, Erray W, Detyniecki M (2015) Random-shapelet: an algorithm for fast shapelet discovery. In: IEEE international conference on data science and advanced analytics, pp 1–10

Rifkin RM, Lippert RA (2007) Notes on regularized least squares. Technical report, MIT

Saxe A, Koh PW, Chen Z, Bhand M, Suresh B, Ng A (2011) On random weights and unsupervised feature learning. In: Getoor L, Scheffer T (eds) Proceedings of the 28th international conference on machine learning, Omnipress, USA, pp 1089–1096

Schäfer P (2015) The BOSS is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29(6):1505–1530MathSciNetCrossRef

Schäfer P, Leser U (2017) Fast and accurate time series classification with WEASEL. In: Proceedings of the 2017 ACM conference on information and knowledge management, pp 637–646

Shifaz A, Pelletier C, Petitjean F, Webb GI (2020) TS-CHIEF: a scalable and accurate forest algorithm for time series classification. Data Min Knowl Discov 34:742–775MathSciNetCrossRef

Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 international joint conference on neural networks, pp 1578–1585

Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification. arXiv:1503.05018

Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. MIT Press, Cambridge, pp 3320–3328

Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions. In: Fourth international conference on learning representations. arXiv:1511.07122

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) European conference on computer vision. Springer, Cham, pp 818–833

Titel: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
verfasst von: Angus Dempster
François Petitjean
Geoffrey I. Webb
Publikationsdatum: 13.07.2020
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 5/2020
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-020-00701-z

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