Abstract
Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Reiss, A., Stricker, D.: Introducing a modular activity monitoring system. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 5621–5624 (2011)
Kampouraki, A., Manis, G., Nikou, C.: Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine 13(4), 512–518 (2009)
Haselsteiner, E., Pfurtscheller, G.: Using time-dependent neural networks for EEG classification. IEEE Transactions on Rehabilitation Engineering 8(4), 457–463 (2000)
Batista, G.E.A.P.A., Wang, X., Keogh, E.J.: A complexity-invariant distance measure for time series. In: SIAM Conf. Data Mining (2011)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proceedings of the VLDB Endowment, vol. 1(2), pp. 1542–1552 (2008)
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, KDD 2012, p. 262 (2012)
Xi, X., Keogh, E.J., Shelton, C.R., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: International Conference on Machine Learning, pp. 1033–1040 (2006)
Xing, Z., Pei, J., Keogh, E.J.: A brief survey on sequence classification. Sigkdd Explorations 12(1), 40–48 (2010)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. arXiv preprint arXiv:1206.5538 (2012)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks 3361 (1995)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253–256. IEEE (2010)
Hu, B., Chen, Y., Keogh, E.: Time Series Classification under More Realistic Assumptions. In: SIAM International Conference on Data Mining, p. 578 (2013)
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: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
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, pp. 947–956. ACM (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient backProp. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998)
Bouvrie, J.: Notes on convolutional neural networks (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, 1106–1114 (2012)
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, ICML 2013, Atlanta, GA, USA, June 16-21, vol. 28 (2013)
Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering Homepage (2011), http://www.cs.ucr.edu/~eamonn/time_series_data/
Pinto, N., Cox, D.D., DiCarlo, J.J.: Why is real-world visual object recognition hard? PLoS Computational Biology 4(1), e27 (2008)
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, pp. 289–297 (2012)
Nanopoulos, A., Alcock, R.O.B., Manolopoulos, Y.: Feature-based classification of time-series data. Information Processing and Technology 0056, 49–61 (2001)
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, pp. 609–616. ACM (2009)
Lee, H., Largman, Y., Pham, P., Ng, A.Y.: Unsupervised Feature Learning for Audio Classification using Convolutional Deep Belief Networks. Advances in Neural Information Processing Systems 22, 1096–1104 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L. (2014). Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-08010-9_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
eBook Packages: Computer ScienceComputer Science (R0)