Castor: Competing shapelets for fast and accurate time series classification

Shapelets are discriminative subsequences, originally embedded in shapelet-based decision trees but have since been extended to shapelet-based transformations. We propose Castor, a simple, efficient, and accurate time series classification algorithm that utilizes shapelets to transform time series. The transformation organizes shapelets into groups with varying dilation and allows the shapelets to compete over the time context to construct a diverse feature representation. By organizing the shapelets into groups, we enable the transformation to transition between levels of competition, resulting in methods that more closely resemble distance-based transformations or dictionary-based transformations. We demonstrate, through an extensive empirical investigation, that Castor yields transformations that result in classifiers that are significantly more accurate than several state-of-the-art classifiers. In an extensive ablation study, we examine the effect of choosing hyperparameters and suggest accurate and efficient default values.

Distributional Data Augmentation Methods for Low Resource Language

Text augmentation is a technique for constructing synthetic data from an under-resourced corpus to improve predictive performance. Synthetic data generation is common in numerous domains. However, recently text augmentation has emerged in natural language processing (NLP) to improve downstream tasks. One of the current state-of-the-art text augmentation techniques is easy data augmentation (EDA), which augments the training data by injecting and replacing synonyms and randomly permuting sentences. One major obstacle with EDA is the need for versatile and complete synonym dictionaries, which cannot be easily found in low-resource languages. To improve the utility of EDA, we propose two extensions, easy distributional data augmentation (EDDA) and type specific similar word replacement (TSSR), which uses semantic word context information and part-of-speech tags for word replacement and augmentation. In an extensive empirical evaluation, we show the utility of the proposed methods, measured by F1 score, on two representative datasets in Swedish as an example of a low-resource language. With the proposed methods, we show that augmented data improve classification performances in low-resource settings.