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
this https URL

提出了 Similarity-Aware Time-Series Classification (SimTSC) 框架，使用图神经网络建模时间序列的相似度信息，并结合深度学习模型实现时序分类。使用 ResNet 作为主干网络和 Dynamic Time Warping 作为相似度度量，在多个数据集上进行了广泛的实验，证明了该方法的有效性。