Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popular. Recently, Similarity-aware Time Series Classification (SimTSC) is proposed to address this problem by using a graph neural network classification model on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data. It shows excellent accuracy and outperforms state-of-the-art deep learning models in several few-label settings. However, since SimTSC relies on pairwise DTW distances, the quadratic complexity of DTW limits its usability to only reasonably sized datasets. To address this challenge, we propose a new efficient semi-supervised time series classification technique, LB-SimTSC, with a new graph construction module. Instead of using DTW, we propose to utilize a lower bound of DTW, LB_Keogh, to approximate the dissimilarity between instances in linear time, while retaining the relative proximity relationships one would have obtained via computing DTW. We construct the pairwise distance matrix using LB_Keogh and build a graph for the graph neural network. We apply this approach to the ten largest datasets from the well-known UCR time series classification archive. The results demonstrate that this approach can be up to 104x faster than SimTSC when constructing the graph on large datasets without significantly decreasing classification accuracy.
翻译:时间序列分类是一个重要的数据挖掘任务,在过去二十年中引起了人们的极大兴趣。 时间序列分类是一个重要的数据挖掘任务,在过去二十年中,这在几个标签环境中引起了很大的兴趣。 但是,由于标签缺乏,半监督的时间序列分类,只有几个标签样本的半监督时间序列的分类变得很受欢迎。 最近,为了解决这个问题,建议使用对称动态时间转换(DTW)距离批量数据生成的图形神经网络分类模型(SIMTSC)来解决这个问题,在图形上使用一个图形式神经网络分类模型(图形式神经网络分类模型),在几个标签设置中,它显示极精密的准确度和优于最先进的104度的深度学习模型。 然而,由于SIMTSC依赖双向的 DTW距离,DW的半监督时间序列比较复杂,因此DTW的可限制其可用性仅限于合理规模的数据集。 为了应对这一挑战,我们建议采用新的图形系统构建模块,而不是使用更精确的DW,我们建议使用更精确的缩缩缩缩缩图,我们使用这个网络的缩略的缩图构建了一个数据库。