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.
@cite_13 , the authors proposed to extend the slicing window technique with a warping window that generates synthetic time series by warping the data through time. This method was used to improve the classification of their deep CNN for TSC, which was also shown to significantly decrease the accuracy of a NN-DTW classifier when compared to our adopted data augmentation algorithm @cite_3 . We should note that the use of a window slicing technique means that the model should classify each subsequence alone and then finally classify the whole time series using a majority voting approach. Alternatively, our method does not crop time series into shorter subsequences which enables the network to learn discriminative properties from the whole time series in an end-to-end manner.
@cite_13 @cite_3
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The research community seems to have converged in agreement that for time series classification problems, Dynamic Time Warping (DTW)-based nearest-neighbor classifiers are exceptionally hard to beat. Obtaining the best performance from DTW requires setting its only parameter, the warping window width (w). This is typically set by cross validation in the training stage. However, for clustering, by definition we do not have access to such labeled data. This issue seems to have been largely ignored in the literature, with many practitioners simply assuming that "the larger the better" for the value of w, and using as large a value of w as computational resources permit. In this work we show that this is a naive approach which in most circumstances produces inferior clusterings. To address this problem, we introduce a novel semi-supervised technique that allows us to set the best value of w. Unlike virtually all other semi-supervised techniques, our ideas are completely independent of the clustering algorithm used, and can be utilized to improve time series clustering under partitional, hierarchical, spectral or density-based clustering. Our approach requires very little human intervention; moreover, we show that in many cases, true human annotation efforts can be replaced with automatically-generated "pseudo" supervision information. We demonstrate our technique by testing with more than one hundred publicly available datasets. Dynamic Time Warping (DTW) is one of the best similarity measures for time series, and it has extensively been used in retrieval, classification or mining applications. It is a costly measure, and applying it to numerous and or very long times series is difficult in practice. Recently, Shapelet Transform (ST) proved to enable accurate supervised classification of time series. ST learns small subsequences that well discriminate classes, and transforms the time series into vectors lying in a metric space. In this paper, we adopt the ST framework in a novel way: we focus on learning, without class label information, shapelets such that Euclidean distances in the ST-space approximate well the true DTW. Our approach leads to an ubiquitous representation of time series in a metric space, where any machine learning method (supervised or unsupervised) and indexing system can operate efficiently.