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Communication Dans Un Congrès Année : 2017

Generating Synthetic Time Series to Augment Sparse Datasets

Résumé

In machine learning, data augmentation is the process of creating synthetic examples in order to augment a dataset used to learn a model. One motivation for data augmentation is to reduce the variance of a classifier, thereby reducing error. In this paper, we propose new data augmentation techniques specifically designed for time series classification, where the space in which they are embedded is induced by Dynamic Time Warping (DTW). The main idea of our approach is to average a set of time series and use the average time series as a new synthetic example. The proposed methods rely on an extension of DTW Barycentric Averaging (DBA), the averaging technique that is specifically developed for DTW. In this paper, we extend DBA to be able to calculate a weighted average of time series under DTW. In this case, instead of each time series contributing equally to the final average, some can contribute more than others. This extension allows us to generate an infinite number of new examples from any set of given time series. To this end, we propose three methods that choose the weights associated to the time series of the dataset. We carry out experiments on the 85 datasets of the UCR archive and demonstrate that our method is particularly useful when the number of available examples is limited (e.g. 2 to 6 examples per class) using a 1-NN DTW classifier. Furthermore, we show that augmenting full datasets is beneficial in most cases, as we observed an increase of accuracy on 56 datasets, no effect on 7 and a slight decrease on only 22.
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Dates et versions

hal-03807627 , version 1 (10-10-2022)

Identifiants

Citer

Germain Forestier, Francois Petitjean, Hoang Anh Dau, Geoffrey Webb, Eamonn Keogh. Generating Synthetic Time Series to Augment Sparse Datasets. 2017 IEEE International Conference on Data Mining (ICDM), Nov 2017, New Orleans, France. pp.865-870, ⟨10.1109/icdm.2017.106⟩. ⟨hal-03807627⟩

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