Data Augmentation for Time Series Classification using Convolutional Neural Networks

Time series classification has been around for decades in the data-mining and machine learning communities. In this paper, we investigate the use of convolutional neural networks (CNN) for time series classification. Such networks have been widely used in many domains like computer vision and speech recognition, but only a little for time series classification. We design a convolu-tional neural network that consists of two convolutional layers. One drawback with CNN is that they need a lot of training data to be efficient. We propose two ways to circumvent this problem: designing data-augmentation techniques and learning the network in a semi-supervised way using training time series from different datasets. These techniques are experimentally evaluated on a benchmark of time series datasets.

[1]  James Large,et al.  The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version , 2016, ArXiv.

[2]  Saeid Nahavandi,et al.  Bag-of-words representation for biomedical time series classification , 2012, Biomed. Signal Process. Control..

[3]  Marco Cuturi,et al.  Fast Global Alignment Kernels , 2011, ICML.

[4]  Yixin Chen,et al.  Multi-Scale Convolutional Neural Networks for Time Series Classification , 2016, ArXiv.

[5]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Yi Zheng,et al.  Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks , 2014, WAIM.

[8]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[9]  Simon Malinowski,et al.  Dense Bag-of-Temporal-SIFT-Words for Time Series Classification , 2016, ArXiv.

[10]  Eamonn J. Keogh,et al.  Three Myths about Dynamic Time Warping Data Mining , 2005, SDM.

[11]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[12]  Andrew G. Howard,et al.  Some Improvements on Deep Convolutional Neural Network Based Image Classification , 2013, ICLR.

[13]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[14]  Jason Lines,et al.  Time series classification with ensembles of elastic distance measures , 2015, Data Mining and Knowledge Discovery.

[15]  Sergey Malinchik,et al.  SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model , 2013, 2013 IEEE 13th International Conference on Data Mining.

[16]  Patrick Schäfer The BOSS is concerned with time series classification in the presence of noise , 2014, Data Mining and Knowledge Discovery.