We propose E2USD that enables efficient-yet-accurate unsupervised MTS state detection. E2USD exploits a Fast Fourier Transform-based Time Series Compressor (FFTCompress) and a Decomposed Dual-view Embedding Module (DDEM) that together encode input MTSs at low computational overhead. Additionally, we propose a False Negative Cancellation Contrastive Learning method (FNCCLearning) to counteract the effects of false negatives and to achieve more cluster-friendly embedding spaces. To reduce computational overhead further in streaming settings, we introduce Adaptive Threshold Detection (ADATD). Comprehensive experiments with six baselines and six datasets offer evidence that E2USD is capable of SOTA accuracy at significantly reduced computational overhead. Our code is available at https://github.com/AI4CTS/E2Usd.

翻译：我们提出E2USD，用于实现高效且准确的无监督多变量时间序列状态检测。E2USD利用基于快速傅里叶变换的时间序列压缩器（FFTCompress）和解耦双视图嵌入模块（DDEM），两者协同以低计算开销对输入多变量时间序列进行编码。此外，我们提出一种假负例消除对比学习方法（FNCCLearning），用于抵消假负例的影响并实现更利于聚类的嵌入空间。为进一步降低流式场景下的计算开销，我们引入了自适应阈值检测方法（ADATD）。基于六种基线方法和六个数据集的综合实验表明，E2USD能够在显著降低计算开销的同时达到最优精度。我们的代码已开源至https://github.com/AI4CTS/E2Usd。