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InceptionTime: Finding AlexNet for time series classification

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Abstract

This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in \(O(N^2\cdot T^4)\) for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with \(N=1500\) time series of short length \(T=46\). Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime—an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1500 time series in one hour but it can also learn from 8M time series in 13 h, a quantity of data that is fully out of reach of HIVE-COTE.

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Notes

  1. https://github.com/hfawaz/InceptionTime.

References

  • Bagnall A, Lines J, Hills J, Bostrom A (2016) Time-series classification with COTE: the collective of transformation-based ensembles. In: International conference on data engineering, pp 1548–1549

  • 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):606–660

    Article  MathSciNet  Google Scholar 

  • Benavoli A, Corani G, Mangili F (2016) Should we really use post-hoc tests based on mean-ranks? Mach Learn Res 17(1):152–161

    MathSciNet  MATH  Google Scholar 

  • Brunel A, Pasquet J, Pasquet J, Rodriguez N, Comby F, Fouchez D, Chaumont M (2019) A CNN adapted to time series for the classification of Supernovae. In: Electronic imaging

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

  • Cuturi M, Blondel M (2017) Soft-dtw: a differentiable loss function for time-series. In: International conference on machine learning, pp 894–903

  • Dau HA, Bagnall A, Kamgar K, Yeh CCM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2018) The ucr time series archive. ArXiv

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

    MathSciNet  MATH  Google Scholar 

  • Forestier G, Petitjean F, Senin P, Despinoy F, Huaulmé A, Ismail Fawaz H, Weber J, Idoumghar L, Muller PA, Jannin P (2018) Surgical motion analysis using discriminative interpretable patterns. Artif Intell Med 91:3–11

    Article  Google Scholar 

  • Friedman M (1940) A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann Math Stat 11(1):86–92

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  • Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feed forward neural networks. In: International conference on artificial intelligence and statistics vol 9, pp 249–256

  • Guan C, Wang X, Zhang Q, Chen R, He D, Xie X (2019) Towards a deep and unified understanding of deep neural models in NLP. In: International conference on machine learning, pp 2454–2463

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778

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

    Article  MathSciNet  Google Scholar 

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 4700–4708

  • Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Transfer learning for time series classification. In: IEEE international conference on big data, pp 1367–1376

  • Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019a) Adversarial attacks on deep neural networks for time series classification. In: IEEE international joint conference on neural networks

  • Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019b) Deep learning for time series classification: a review. Data Min Knowl Discov

  • Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019c) Deep neural network ensembles for time series classification. In: IEEE international joint conference on neural networks

  • Ismail Fawaz H, Forestier G, Weber J, Petitjean F, Idoumghar L, Muller PA (2019d) Automatic alignment of surgical videos using kinematic data. In: Artificial intelligence in medicine, pp 104–113

  • Karimi-Bidhendi S, Munshi F, Munshi A (2018) Scalable classification of univariate and multivariate time series. In: IEEE international conference on big data, pp 1598–1605

  • Kashiparekh K, Narwariya J, Malhotra P, Vig L, Shroff G (2019) Convtimenet: A pre-trained deep convolutional neural network for time series classification. In: IEEE international joint conference on neural networks

  • Keogh EJ, Pazzani MJ (2001) Derivative dynamic time warping. In: Proceedings of the 2001 SIAM international conference on data mining, SIAM, pp 1–11

  • Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: International conference on learning representations

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  • Le Guennec A, Malinowski S, Tavenard R (2016) Data augmentation for time series classification using convolution neural networks. In: ECML/PKDD workshop on advanced analytics and learning on temporal data

  • LeCun Y, Bottou L, Orr GB, Müller KR (1998) Efficient backprop. In: Neural networks: tricks of the trade, this book is an outgrowth of a 1996 NIPS workshop, pp 9–50

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  • Lee W, Park S, Joo W, Moon IC (2018) Diagnosis prediction via medical context attention networks using deep generative modeling. In: IEEE international conference on data mining, pp 1104–1109

  • Lines J, Bagnall A (2015) Time series classification with ensembles of elastic distance measures. Data Min Knowl Disc 29(3):565–592

    Article  MathSciNet  Google Scholar 

  • Lines J, Taylor S, Bagnall A (2016) HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification. In: IEEE international conference on data mining, pp 1041–1046

  • Liu Y, Yu J, Han Y (2018) Understanding the effective receptive field in semantic image segmentation. Multimed Tools Appl 77(17):22159–22171

    Article  Google Scholar 

  • 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):607–635

    Article  Google Scholar 

  • Luo W, Li Y, Urtasun R, Zemel R (2016) Understanding the effective receptive field in deep convolutional neural networks. In: Advances in neural information processing systems, pp 4898–4906

  • Marteau P (2009) Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans Pattern Anal Mach Intell 31(2):306–318

    Article  Google Scholar 

  • Pelletier C, Webb GI, Petitjean F (2019) Temporal convolutional neural network for the classification of satellite image time series. Remote Sens 11(5):523

    Article  Google Scholar 

  • Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  • Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

  • Scardapane S, Wang D (2017) Randomness in neural networks: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 7(2):e1200

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Schäfer P (2015b) Scalable time series classification. Data Min Knowl Discov, pp 1–26

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

  • Stefan A, Athitsos V, Das G (2013) The move-split-merge metric for time series. IEEE Trans Knowl Data Eng 25(6):1425–1438

    Article  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  • Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI conference on artificial intelligence

  • Tan CW, Webb GI, Petitjean F (2017) Indexing and classifying gigabytes of time series under time warping. In: Proceedings of the 2017 SIAM international conference on data mining, SIAM, pp 282–290

  • Vlachos M, Hadjieleftheriou M, Gunopulos D, Keogh E (2006) Indexing multidimensional time-series. VLDB J Int J Very Large Data Bases 15(1):1–20

    Article  Google Scholar 

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

  • Yi F, Yu Z, Zhuang F, Zhang X, Xiong H (2018) An integrated model for crime prediction using temporal and spatial factors. In: IEEE international conference on data mining, pp 1386–1391

  • Yuan Y, Xun G, Ma F, Wang Y, Du N, Jia K, Su L, Zhang A (2018) Muvan: A multi-view attention network for multivariate temporal data. In: IEEE international conference on data mining, pp 717–726

  • Zhang C, Tavanapong W, Kijkul G, Wong J, de Groen PC, Oh J (2018) Similarity-based active learning for image classification under class imbalance. In: IEEE international conference on data mining, pp 1422–1427

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Acknowledgements

The authors would like to thank the creators and providers of the datasets. The authors would also like to thank NVIDIA Corporation for the GPU Grant and the Mésocentre of Strasbourg for providing access to the cluster. This work was supported by the ANR TIMES project (Grant ANR-17-CE23-0015) of the French Agence Nationale de la Recherche. François Petitjean is the recipient of an Australian Research Council Discovery Early Career Award (Project Number DE170100037) funded by the Australian Government. This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award Number FA2386-18-1-4030.

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Authors and Affiliations

  1. Université de Haute-Alsace, IRIMAS UR 7499, 68100, Mulhouse, France

    Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar & Pierre-Alain Muller

  2. Faculty of IT, Monash University, Melbourne, Australia

    Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb & François Petitjean

  3. IRISA, UMR CNRS 6074, Université Bretagne Sud, Vannes, France

    Charlotte Pelletier

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  1. Hassan Ismail Fawaz
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  2. Benjamin Lucas
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  3. Germain Forestier
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Correspondence to Hassan Ismail Fawaz.

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Ismail Fawaz, H., Lucas, B., Forestier, G. et al. InceptionTime: Finding AlexNet for time series classification. Data Min Knowl Disc 34, 1936–1962 (2020). https://doi.org/10.1007/s10618-020-00710-y

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