We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner. Motivated by the different working mechanisms within these two research lines, we aim to connect them in such a way as to jointly model time-series similarities and learn the representations. This is a challenging task because it is unclear how we should efficiently leverage similarity information. To tackle the challenge, we propose Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and general framework that models similarity information with graph neural networks (GNNs). Specifically, we formulate TSC as a node classification problem in graphs, where the nodes correspond to time-series, and the links correspond to pair-wise similarities. We further design a graph construction strategy and a batch training algorithm with negative sampling to improve training efficiency. We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping (DTW) as the similarity measure. Extensive experiments on the full UCR datasets and several multivariate datasets demonstrate the effectiveness of incorporating similarity information into deep learning models in both supervised and semi-supervised settings. Our code is available at https://github.com/daochenzha/SimTSC
翻译:我们研究时间序列分类(TSC),这是时间序列数据挖掘的一项基本任务。以前的工作从两个主要方向接近TSC: (1) 基于时间序列的类似方法,根据最近的邻居对时间序列分类进行分类,(2) 直接学习数据驱动方式分类代表的深层次学习模式。我们受这两个研究线内不同工作机制的驱动,我们的目标是将它们联系起来,以便共同模拟时间序列相似性,并了解这些表述。这是一项具有挑战性的任务,因为我们不知道如何有效地利用相似性信息。为了应对这一挑战,我们建议采用类似性-Aware时间序列分类(SimTSC),这是一个概念简单和一般的框架,用来模拟与图形神经网络(GNNS)的相似性信息。具体地说,我们把TSC作为图表中的节点分类问题,将节点与时间序列相对应,并将联系与对等相似的相似性。我们进一步设计图表构建战略和配有负面抽样的批量培训算法,以提高培训效率。我们用ResNet将SimTSC作为UMS-动态时间序列(DTH)的骨架和动态同步性时间同步测试(DTIS-CRS-CRislustyal Steal sestal Studal sess)的多种数据测试,将多种数据都显示为我们现有的数据模型。