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.

翻译：我们提出E2USD方法，可在多变量时间序列上进行高效且准确的无监督状态检测。E2USD利用基于快速傅里叶变换的时间序列压缩器（FFTCompress）与解耦双视角嵌入模块（DDEM），以较低计算开销协同编码输入的多变量时间序列。此外，我们提出一种假阴性消除对比学习方法（FNCCLearning），用于抵消假负样本的影响，从而获得更具聚类友好性的嵌入空间。为降低流式场景下的计算开销，我们引入自适应阈值检测（ADATD）。基于六个基线方法与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实现最先进的检测精度。