Data augmentation using synthetic data for time series classification with deep residual networks

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN's accuracy on some datasets and significantly improve the deep model's accuracy when the method is used in an ensemble approach.

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

[2]  Geoffrey I. Webb,et al.  Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification , 2014, 2014 IEEE International Conference on Data Mining.

[3]  Marco Cuturi,et al.  Soft-DTW: a Differentiable Loss Function for Time-Series , 2017, ICML.

[4]  Dana Kulic,et al.  Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks , 2017, ICMI.

[5]  Romain Tavenard,et al.  Data Augmentation for Time Series Classification using Convolutional Neural Networks , 2016 .

[6]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Mario Michael Krell,et al.  Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data , 2018, ArXiv.

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Kjersti Aas,et al.  Predicting mortgage default using convolutional neural networks , 2018, Expert Syst. Appl..

[11]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[12]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[13]  Razvan Pascanu,et al.  Deep Learners Benefit More from Out-of-Distribution Examples , 2011, AISTATS.

[14]  Xinyu Luo,et al.  Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification , 2018, ArXiv.

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

[16]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[17]  Eamonn J. Keogh,et al.  The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2016, Data Mining and Knowledge Discovery.

[18]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..

[19]  Geoffrey I. Webb,et al.  Generating Synthetic Time Series to Augment Sparse Datasets , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[20]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[21]  Geoffrey I. Webb,et al.  Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm , 2015, Knowledge and Information Systems.