Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

Yi Zheng1,2, Qi Liu2, Enhong Chen2, Yong Ge3, Jinxi Zhao1
1Department of Information Systems, City University of Hong Kong, Hong Kong, China
2School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
3Department of Computer Science, University of North Carolina at Charlotte, Charlotte, USA

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Xing Z, Pei J, Keogh E. A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter, 2010, 12(1): 40–48

Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E. Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment, 2008, 1(2): 1542–1552

Orsenigo C, Vercellis C. Combining discrete svm and fixed cardinality warping distances for multivariate time series classification. Pattern Recognition, 2010, 43(11): 3787–3794

Batal I, Sacchi L, Bellazzi R, Hauskrecht M. Multivariate time series classification with temporal abstractions. In: Proceedings of FLAIRS Conference. 2009

Haselsteiner E, Pfurtscheller G. Using time-dependent neural networks for EEG classification. IEEE Transactions on Rehabilitation Engineering, 2000, 8(4): 457–463

Kampouraki A, Manis G, Nikou C. Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(4): 512–518

Reiss A, Stricker D. Introducing a modular activity monitoring system. In: Proceedings of IEEE Annual International Conference on Engineering in Medicine and Biology Society. 2011, 5621–5624

Batista G E A P A, Wang X, Keogh E J. A complexity-invariant distance measure for time series. In: Proceedings of SIAM Conference on Data Mining. 2011

Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E. Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 262–270

Xi X, Keogh E J, Shelton C R, Wei L, Ratanamahatana C A. Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 1033–1040

Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798–1828

LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 1995, 3361(10)

LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proceedings of IEEE International Symposium on Circuits and Systems. 2010, 253–256

Zheng Y, Liu Q, Chen E, Ge Y, Zhao J. Time series classification using multi-channels deep convolutional neural networks. In: Proceedings of the 15th International Conference on Web-Age Information Management. 2014, 298–310

Hu B, Chen Y, Keogh E. Time Series Classification under More Realistic Assumptions. In: Proceedings of SIAM International Conference on Data Mining. 2013, 578

Goldberger A L, Amaral L A N, Glass L, Hausdorff J M, Ivanov P C, Mark R G, Mietus J E, Moody G B, Peng C K, Stanley H E. Physiobank, Physiotoolkit, and Physionet omponents of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): e215–e220

Ye L, Keogh E. Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 947–956

Ratanamahatana C A, Keogh E. Making time-series classification more accurate using learned constraints. In: Proceedings of SIAM International Conference on Data Mining. 2004

Ratanamahatana C A, Keogh E. Three myths about dynamic time warping data mining. In: Proceedings of SIAM International Conference on Data Mining. 2005, 506–510

Yu D, Yu X, Hu Q, Liu J, Wu A. Dynamic time warping constraint learning for large margin nearest neighbor classification. Information Sciences, 2011, 181(13): 2787–2796

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324

Simard P Y, Steinkraus D, Platt J C. Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the 7th International Conference on Document Analysis and Recognition. 2003, 958–962

Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference onMachine Learning. 2010, 807–814

Zeiler M D, Ranzato M, Monga R, Mao M, Yang K, Le Q, Nguyen P, Senior A, Vanhoucke V, Dean J, Hinton G E. On rectified linear units for speech processing. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2013, 3517–3521

Scherer D, Müller A, Behnke S. Evaluation of pooling operations in convolutional architectures for object recognition. In: Proceedings of the 20th International Conference on Artificial Neural Networks. 2010, 92–101

Nagi J, Ducatelle F, Di Caro G A, Ciresan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella L M. Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: Proceedings of IEEE International Conference on Signal and Image Processing Applications. 2011, 342–347

LeCun Y, Bottou L, Orr G B, Müller K R. Efficient backprop. Lecture Notes in Computer Science, 2012, 7700: 9–48

Bouvrie J. Notes on convolutional neural networks. Technical Report. 2006

Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. 2012, 1106–1114

Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning. 2013, 1139–1147

Erhan D, Bengio Y, Courville A, Manzagol P A, Vincent P, Bengio S. Why does unsupervised pre-training help deep learning? The Journal of Machine Learning Research, 2010, 11: 625–660

Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507

Masci J, Meier U, Cireşan D, Schmidhuber J. Stacked convolutional auto-encoders for hierarchical feature extraction. Lecture Notes in Computer Science, 2011, 6791: 52–59

Pinto N, Cox D D, DiCarlo J J. Why is real-world visual object recognition hard? PLoS Computational Biology, 2008, 4(1): e27

Cireşan D C, Meier U, Masci J, Gambardella L M, Schmidhuber J. Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 1237–1242

Cireşan D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural Networks, 2012, 32: 333–338

Lines J, Davis L M, Hills J, Bagnall A. A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 289–297

Nanopoulos A, Alcock R O B, Manolopoulos Y. Feature-based classification of time-series data. International Journal of Computer Research, 2001, 10(3)

Lee H, Grosse R, Ranganath R, Ng A Y. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning. 2009, 609–616

Lee H, Largman Y, Pham P, Ng A Y. Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Proceedings of Advances in Neural Information Processing Systems. 2009, 1096–1104

Waibel A, Hanazawa T, Hinton G, Shikano K, Lang K J. Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing, 1989, 37(3): 328–339