research-article

Deep learning for time series classification: a review

Authors:

Hassan Ismail Fawaz,

Germain Forestier,

Jonathan Weber,

Lhassane Idoumghar,

Pierre-Alain MullerAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 33, Issue 4

Pages 917 - 963

https://doi.org/10.1007/s10618-019-00619-1

Published: 01 July 2019 Publication History

Abstract

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

References

[1]

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Accessed 28 Feb 2019

[2]

Al-Jowder O, Kemsley E, and Wilson R Mid-infrared spectroscopy and authenticity problems in selected meats: a feasibility study Food Chem 1997 59 2 195-201

[3]

Aswolinskiy W, Reinhart RF, Steil J (2016) Time series classification in reservoir- and model-space: a comparison. In: Artificial neural networks in pattern recognition, pp 197–208

[4]

Aswolinskiy W, Reinhart RF, and Steil J Time series classification in reservoir- and model-space Neural Process Lett 2017 48 789-809

[5]

Bagnall A and Janacek G A run length transformation for discriminating between auto regressive time series J Classif 2014 31 2 154-178

[6]

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

[7]

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

[8]

Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: International conference on learning representations

[9]

Baird HS Document image defect models 1992 Berlin Springer 546-556

[10]

Banerjee D, Islam K, Mei G, Xiao L, Zhang G, Xu R, Ji S, Li J (2017) A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. In: IEEE international conference on data mining, pp 11–20

[11]

Baydogan MG (2015) Multivariate time series classification datasets. http://www.mustafabaydogan.com. Accessed 28 Feb 2019

[12]

Baydogan MG, Runger G, and Tuv E A bag-of-features framework to classify time series IEEE Trans Pattern Anal Mach Intell 2013 35 11 2796-2802

[13]

Bellman R Dynamic programming 2010 Princeton Princeton University Press

[14]

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

[15]

Bengio Y, Yao L, Alain G, Vincent P (2013) Generalized denoising auto-encoders as generative models. In: International conference on neural information processing systems, pp 899–907

[16]

Bianchi FM, Scardapane S, Løkse S, Jenssen R (2018) Reservoir computing approaches for representation and classification of multivariate time series. arXiv:1803.07870

[17]

Bishop C Pattern recognition and machine learning 2006 Berlin Springer

[18]

Bostrom A, Bagnall A (2015) Binary shapelet transform for multiclass time series classification. In: Big data analytics and knowledge discovery, pp 257–269

[19]

Che Z, Cheng Y, Zhai S, Sun Z, Liu Y (2017a) Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In: IEEE international conference on data mining, pp 787–792

[20]

Che Z, He X, Xu K, Liu Y (2017b) DECADE: a deep metric learning model for multivariate time series. In: KDD workshop on mining and learning from time series

[21]

Chen H, Tang F, Tino P, Yao X (2013) Model-based kernel for efficient time series analysis. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 392–400

[22]

Chen H, Tang F, Tiño P, Cohn A, Yao X (2015a) Model metric co-learning for time series classification. In: International joint conference on artificial intelligence, pp 3387–394

[23]

Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2015b) The UCR time series classification archive. www.cs.ucr.edu/~eamonn/time_series_data/. Accessed 28 Feb 2019

[24]

Chollet Fea (2015) Keras. https://keras.io. Accessed 28 Feb 2019

[25]

Chouikhi N, Ammar B, Alimi AM (2018) Genesis of basic and multi-layer echo state network recurrent autoencoders for efficient data representations. arXiv:1804.08996

[26]

Cristian Borges Gamboa J (2017) Deep learning for time-series analysis. arXiv:1701.01887

[27]

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

[28]

Dau HA, Silva DF, Petitjean F, Forestier G, Bagnall A, Keogh E (2017) Judicious setting of dynamic time warping’s window width allows more accurate classification of time series. In: IEEE international conference on big data, pp 917–922

[29]

Dau HA, Bagnall A, Kamgar K, Yeh CCM, Zhu Y, Gharghabi S, Ratanamahatana CA, Keogh E (2018) The UCR time series archive. ArXiv arXiv:1810.07758

[30]

Demšar J Statistical comparisons of classifiers over multiple data sets Mach Learn Res 2006 7 1-30

[31]

Deng H, Runger G, Tuv E, and Vladimir M A time series forest for classification and feature extraction Inf Sci 2013 239 142-153

[32]

Esling P and Agon C Time-series data mining ACM Comput Surv 2012 45 1 12:1-12:34

[33]

Faust O, Hagiwara Y, Hong TJ, Lih OS, and Acharya UR Deep learning for healthcare applications based on physiological signals: a review Comput Methods Programs Biomed 2018 161 1-13

[34]

Forestier G, Petitjean F, Dau HA, Webb GI, Keogh E (2017) Generating synthetic time series to augment sparse datasets. In: IEEE international conference on data mining, pp 865–870

[35]

Friedman M A comparison of alternative tests of significance for the problem of

m

rankings Ann Math Stat 1940 11 1 86-92

[36]

Gallicchio C, Micheli A (2017) Deep echo state network (DeepESN): a brief survey. arXiv:1712.04323

[37]

Garcia S and Herrera F An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons Mach Learn Res 2008 9 2677-2694

[38]

Geng Y, Luo X (2018) Cost-sensitive convolution based neural networks for imbalanced time-series classification. ArXiv arXiv:1801.04396

[39]

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

[40]

Goldberg Y A primer on neural network models for natural language processing Artif Intell Res 2016 57 1 345-420

[41]

Gong Z, Chen H, Yuan B, and Yao X Multiobjective learning in the model space for time series classification IEEE Trans Cybern 2018 99 1-15

[42]

Grabocka J, Schilling N, Wistuba M, Schmidt-Thieme L (2014) Learning time-series shapelets. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 392–401

[43]

Hatami N, Gavet Y, Debayle J (2017) Classification of time-series images using deep convolutional neural networks. In: International conference on machine vision

[44]

He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE international conference on computer vision, pp 1026–1034

[45]

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

[46]

Hills J, Lines J, Baranauskas E, Mapp J, and Bagnall A Classification of time series by shapelet transformation Data Min Knowl Discov 2014 28 4 851-881

[47]

Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, and Kingsbury B Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups IEEE Signal Process Mag 2012 29 6 82-97

[48]

Hoerl AE and Kennard RW Ridge regression: applications to nonorthogonal problems Technometrics 1970 12 1 69-82

[49]

Holm S A simple sequentially rejective multiple test procedure Scand J Stat 1979 6 2 65-70

[50]

Höppner F Improving time series similarity measures by integrating preprocessing steps Data Min Knowl Discov 2016 31 851-878

[51]

Hu Q, Zhang R, and Zhou Y Transfer learning for short-term wind speed prediction with deep neural networks Renew Energy 2016 85 83-95

[52]

Ignatov A Real-time human activity recognition from accelerometer data using convolutional neural networks Appl Soft Comput 2018 62 915-922

[53]

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, vol 37, pp 448–456

[54]

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018a) Data augmentation using synthetic data for time series classification with deep residual networks. In: International workshop on advanced analytics and learning on temporal data, ECML PKDD

[55]

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018b) Evaluating surgical skills from kinematic data using convolutional neural networks. In: Medical image computing and computer assisted intervention, pp 214–221

[56]

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

[57]

Jaeger H and Haas H Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication Science 2004 304 5667 78-80

[58]

Kate RJ Using dynamic time warping distances as features for improved time series classification Data Min Knowl Discov 2016 30 2 283-312

[59]

Keogh E, Mueen A (2017) Curse of dimensionality. In: Encyclopedia of Machine Learning and Data Mining. Springer, pp 314–315

[60]

Kim Y (2014) Convolutional neural networks for sentence classification. In: Empirical methods in natural language processing

[61]

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

[62]

Kotsifakos A, Papapetrou P (2014) Model-based time series classification. In: Advances in intelligent data analysis, pp 179–191

[63]

Krasin I, Duerig T, Alldrin N, Ferrari V, Abu-El-Haija S, Kuznetsova A, Rom H, Uijlings J, Popov S, Kamali S, Malloci M, Pont-Tuset J, Veit A, Belongie S, Gomes V, Gupta A, Sun C, Chechik G, Cai D, Feng Z, Narayanan D, Murphy K (2017) OpenImages: a public dataset for large-scale multi-label and multi-class image classification. https://storage.googleapis.com/openimages/web/index.html. Accessed 28 Feb 2019

[64]

Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems vol 25, pp 1097–1105

[65]

Kruskal JB, Wish M (1978) Multidimensional scaling. Number 07–011 in sage university paper series on quantitative applications in the social sciences

[66]

Längkvist M, Karlsson L, and Loutfi A A review of unsupervised feature learning and deep learning for time-series modeling Pattern Recognit Lett 2014 42 11-24

[67]

Large J, Lines J, Bagnall A (2017) The heterogeneous ensembles of standard classification algorithms (HESCA): the whole is greater than the sum of its parts. arXiv:1710.09220

[68]

Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, vol 32, pp II–1188–II–1196

[69]

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

[70]

LeCun Y, Bottou L, Bengio Y, and Haffner P Gradient-based learning applied to document recognition Proc IEEE 1998 86 11 2278-2324

[71]

LeCun Y, Bottou L, Orr GB, and Müller KR Montavon G Efficient backprop Neural networks: tricks of the trade 1998 Berlin Springer 9-50

[72]

LeCun Y, Bengio Y, and Hinton G Deep learning Nature 2015 521 436-444

[73]

Lin S and Runger GC GCRNN: group-constrained convolutional recurrent neural network IEEE Trans Neural Netw Learn Syst 2018 99 1-10

[74]

Lines J and Bagnall A Time series classification with ensembles of elastic distance measures Data Min Knowl Discov 2015 29 3 565-592

[75]

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

[76]

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

[77]

Liu C, Hsaio W, and Tu Y Time series classification with multivariate convolutional neural network IEEE Trans Ind Electron 2018 66 1-1

[78]

Lu J, Young S, Arel I, and Holleman J A 1 tops/w analog deep machine-learning engine with floating-gate storage in 0.13

μ

m cmos IEEE J Solid-State Circuits 2015 50 1 270-281

[79]

Lucas B, Shifaz A, Pelletier C, O’Neill L, Zaidi N, Goethals B, Petitjean F, and Webb GI Proximity forest: an effective and scalable distance-based classifier for time series Data Min Knowl Discov 2018 28 851-881

[80]

Ma Q, Shen L, Chen W, Wang J, Wei J, and Yu Z Functional echo state network for time series classification Inf Sci 2016 373 1-20

[81]

Malhotra P, TV V, Vig L, Agarwal P, Shroff G (2018) TimeNet: pre-trained deep recurrent neural network for time series classification. In: European symposium on artificial neural networks, computational intelligence and machine learning, pp 607–612

[82]

Martinez C, Perrin G, Ramasso E, Rombaut M (2018) A deep reinforcement learning approach for early classification of time series. In: European signal processing conference

[83]

Mehdiyev N, Lahann J, Emrich A, Enke D, Fettke P, and Loos P Time series classification using deep learning for process planning: a case from the process industry Proc Comput Sci 2017 114 242-249

[84]

Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. In: International conference on learning representations—workshop

[85]

Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Neural information processing systems, pp 3111–3119

[86]

Mittelman R (2015) Time-series modeling with undecimated fully convolutional neural networks. arXiv:1508.00317

[87]

Neamtu R, Ahsan R, Rundensteiner EA, Sarkozy G, Keogh E, Dau HA, Nguyen C, Lovering C (2018) Generalized dynamic time warping: unleashing the warping power hidden in point-wise distances. In: IEEE international conference on data engineering

[88]

Nguyen TL, Gsponer S, Ifrim G (2017) Time series classification by sequence learning in all-subsequence space. In: IEEE international conference on data engineering, pp 947–958

[89]

Nwe TL, Dat TH, Ma B (2017) Convolutional neural network with multi-task learning scheme for acoustic scene classification. In: Asia-Pacific signal and information processing association annual summit and conference, pp 1347–1350

[90]

Nweke HF, Teh YW, Al-garadi MA, and Alo UR Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges Expert Syst Appl 2018 105 233-261

[91]

Ordón̈ez FJ and Roggen D Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition Sensors 2016 16 115

[92]

Pan SJ and Yang Q A survey on transfer learning IEEE Trans Knowl Data Eng 2010 22 10 1345-1359

[93]

Papernot N, McDaniel P (2018) Deep k-nearest neighbors: towards confident, interpretable and robust deep learning. arXiv:1803.04765

[94]

Pascanu R, Mikolov T, Bengio Y (2012) Understanding the exploding gradient problem. arXiv:1211.5063

[95]

Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning, vol 28, pp III–1310–III–1318

[96]

Petitjean F, Forestier G, Webb GI, Nicholson AE, Chen Y, and Keogh E Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm Knowl Inf Syst 2016 47 1 1-26

[97]

Poggio T, Mhaskar H, Rosasco L, Miranda B, and Liao Q Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review Int J Autom Comput 2017 14 5 503-519

[98]

Rajan D, Thiagarajan J (2018) A generative modeling approach to limited channel ecg classification. In: IEEE engineering in medicine and biology society, vol 2018, p 2571

[99]

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Liu PJ, Liu X, Sun M, Sundberg P, Yee H, Zhang K, Duggan GE, Flores G, Hardt M, Irvine J, Le Q, Litsch K, Marcus J, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell M, Cui C, Corrado G, and Dean J Scalable and accurate deep learning for electronic health records NPJ Digit Med 2018 1 18

[100]

Ratanamahatana CA, Keogh E (2005) Three myths about dynamic time warping data mining. In: SIAM international conference on data mining, pp 506–510

[101]

Rowe ACH and Abbott PC Daubechies wavelets and mathematica Comput Phys 1995 9 6 635-648

[102]

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

[103]

Sainath TN, Mohamed AR, Kingsbury B, Ramabhadran B (2013) Deep convolutional neural networks for LVCSR. In: IEEE international conference on acoustics, speech and signal processing, pp 8614–8618

[104]

Santos T, Kern R (2017) A literature survey of early time series classification and deep learning. In: International conference on knowledge technologies and data-driven business

[105]

Schäfer P The BOSS is concerned with time series classification in the presence of noise Data Min Knowl Discov 2015 29 6 1505-1530

[106]

Serrà J, Pascual S, and Karatzoglou A Towards a universal neural network encoder for time series Artif Intell Res Dev Curr Chall New Trends Appl 2018 308 120

[107]

Silva DF, Giusti R, Keogh E, and Batista G Speeding up similarity search under dynamic time warping by pruning unpromising alignments Data Min Knowl Discov 2018 32 4 988-1016

[108]

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, and Salakhutdinov R Dropout: a simple way to prevent neural networks from overfitting J Mach Learn Res 2014 15 1929-1958

[109]

Strodthoff N and Strodthoff C Detecting and interpreting myocardial infarction using fully convolutional neural networks Physiol Meas 2019 40 1 015001

[110]

Susto GA, Cenedese A, Terzi M (2018) Time-series classification methods: review and applications to power systems data. In: Big data application in power systems, pp 179 – 220

[111]

Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Neural information processing systems, pp 3104–3112

[112]

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

[113]

Tanisaro P, Heidemann G (2016) Time series classification using time warping invariant echo state networks. In: IEEE international conference on machine learning and applications, pp 831–836

[114]

Tripathy R and Acharya UR Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework Biocybern Biomed Eng 2018 38 890-902

[115]

Uemura M, Tomikawa M, Miao T, Souzaki R, Ieiri S, Akahoshi T, Lefor AK, and Hashizume M Feasibility of an AI-based measure of the hand motions of expert and novice surgeons Comput Math Methods Med 2018 2018 9873273

[116]

Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv:1607.08022

[117]

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

[118]

Wang Z, Oates T (2015a) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at AAAI conference on artificial intelligence, pp 40–46

[119]

Wang Z, Oates T (2015b) Imaging time-series to improve classification and imputation. In: International conference on artificial intelligence, pp 3939–3945

[120]

Wang Z, Oates T (2015c) Spatially encoding temporal correlations to classify temporal data using convolutional neural networks. arXiv:1509.07481

[121]

Wang L, Wang Z, and Liu S An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm Expert Syst Appl 2016 43 237-249

[122]

Wang W, Chen C, Wang W, Rai P, Carin L (2016b) Earliness-aware deep convolutional networks for early time series classification. arXiv:1611.04578

[123]

Wang Z, Song W, Liu L, Zhang F, Xue J, Ye Y, Fan M, Xu M (2016c) Representation learning with deconvolution for multivariate time series classification and visualization. arXiv:1610.07258

[124]

Wang S, Hua G, Hao G, and Xie C A cycle deep belief network model for multivariate time series classification Math Probl Eng 2017 2017 1-7

[125]

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

[126]

Wang J, Chen Y, Hao S, Peng X, Hu L (2018a) Deep learning for sensor-based activity recognition: a survey. Pattern Recognit Lett

[127]

Wang J, Wang Z, Li J, Wu J (2018b) Multilevel wavelet decomposition network for interpretable time series analysis. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2437–2446

[128]

Wilcoxon F Individual comparisons by ranking methods Biom Bull 1945 1 6 80-83

[129]

Yang Q and Wu X 10 challenging problems in data mining research Inf Technol Decis Mak 2006 05 04 597-604

[130]

Ye L and Keogh E Time series shapelets: a novel technique that allows accurate, interpretable and fast classification Data Min Knowl Discov 2011 22 1 149-182

[131]

Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: International conference on neural information processing systems, vol 2, pp 3320–3328

[132]

Zeiler MD (2012) ADADELTA: an adaptive learning rate method. arXiv:1212.5701

[133]

Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2017) Understanding deep learning requires rethinking generalization. In: International conference on learning representations

[134]

Zhao B, Lu H, Chen S, Liu J, and Wu D Convolutional neural networks for time series classification Syst Eng Electron 2017 28 1 162-169

[135]

Zheng Y, Liu Q, Chen E, Ge Y, Zhao JL (2014) Time series classification using multi-channels deep convolutional neural networks. In: Web-age information management, pp 298–310

[136]

Zheng Y, Liu Q, Chen E, Ge Y, and Zhao JL Exploiting multi-channels deep convolutional neural networks for multivariate time series classification Front Comput Sci 2016 10 1 96-112

[137]

Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: IEEE conference on computer vision and pattern recognition, pp 2921–2929

[138]

Ziat A, Delasalles E, Denoyer L, Gallinari P (2017) Spatio-temporal neural networks for space-time series forecasting and relations discovery. In: IEEE international conference on data mining, pp 705–714

Cited By

Zhang KHe MGuo J(2024)An efficient CNN-transformer hybrid approach for water turbine unit failure predictionJournal of Computational Methods in Sciences and Engineering10.3233/JCM-23707424:2(823-834)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-237074
Ang YHuang QBao YTung AHuang Z(2024)TSGBench: Time Series Generation BenchmarkProceedings of the VLDB Endowment10.14778/3632093.363209717:3(305-318)Online publication date: 20-Jan-2024
https://doi.org/10.14778/3632093.3632097
Farokhmanesh FNeuhauser CWestermann R(2024)A Compact and Efficient Neural Data Structure for Mutual Information Estimation in Large TimeseriesProceedings of the 36th International Conference on Scientific and Statistical Database Management10.1145/3676288.3676295(1-12)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3676288.3676295
Show More Cited By

Recommendations

Deep learning based classification of time series of chaotic systems over graphic images
Abstract
In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the ...
Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks
Proceedings of the 23rd International Conference on Artificial Neural Networks and Machine Learning ICANN 2013 - Volume 8131

Artificial neural networks have proved to be good at time-series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings ...
A predictive analytics framework for sensor data using time series and deep learning techniques
Abstract
IoT devices convert billions of objects into data-generating entities, enabling them to report status and interact with their surroundings. This data comes in various formats, like structured, semi-structured, or unstructured. In addition, it can ...

Comments

Information & Contributors

Information

Published In

cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 33, Issue 4

Jul 2019

418 pages

ISSN:1384-5810

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2019

Accepted: 25 February 2019

Received: 12 September 2018

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

427
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang KHe MGuo J(2024)An efficient CNN-transformer hybrid approach for water turbine unit failure predictionJournal of Computational Methods in Sciences and Engineering10.3233/JCM-23707424:2(823-834)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-237074
Ang YHuang QBao YTung AHuang Z(2024)TSGBench: Time Series Generation BenchmarkProceedings of the VLDB Endowment10.14778/3632093.363209717:3(305-318)Online publication date: 20-Jan-2024
https://doi.org/10.14778/3632093.3632097
Farokhmanesh FNeuhauser CWestermann R(2024)A Compact and Efficient Neural Data Structure for Mutual Information Estimation in Large TimeseriesProceedings of the 36th International Conference on Scientific and Statistical Database Management10.1145/3676288.3676295(1-12)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3676288.3676295
Stenger MBauer APrantl TLeppich RHudson NChard KFoster IKounev S(2024)Thinking in Categories: A Survey on Assessing the Quality for Time Series SynthesisJournal of Data and Information Quality10.1145/366600616:2(1-32)Online publication date: 25-May-2024
https://dl.acm.org/doi/10.1145/3666006
Nuttall TSerra XPearson L(2024)Svara-forms and coarticulation in Carnatic music: an investigation using deep clusteringProceedings of the 11th International Conference on Digital Libraries for Musicology10.1145/3660570.3660580(15-22)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3660570.3660580
Errafik yDhassi yKenzi a(2024)Temporal Data Augmentation techniques for sensor-based Human Activity RecognitionProceedings of the 7th International Conference on Networking, Intelligent Systems and Security10.1145/3659677.3659712(1-5)Online publication date: 18-Apr-2024
https://dl.acm.org/doi/10.1145/3659677.3659712
Hong ZLi ZZhong SLyu WWang HDing YHe TZhang D(2024)CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised PretrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595978:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659597
Sridhar HHuang GThorpe AOishi MPitts B(2024)Characterizing the Effect of Mind Wandering on Braking Dynamics in Partially Autonomous VehiclesACM Transactions on Cyber-Physical Systems10.1145/36536788:3(1-21)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3653678
Jiang WGoncalves JKostakos V(2024)Mobile Near-infrared Sensing—A Systematic Review on Devices, Data, Modeling, and ApplicationsACM Computing Surveys10.1145/365259656:8(1-36)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3652596
Mohammadi Foumani NMiller LTan CWebb GForestier GSalehi M(2024)Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyACM Computing Surveys10.1145/364944856:9(1-45)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3649448
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents