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，该方法能够实现高效且精准的无监督多元时间序列状态检测。E2USD采用基于快速傅里叶变换的时间序列压缩器（FFTCompress）和解耦双视角嵌入模块（DDEM），两者协同工作，以较低的计算开销对输入多元时间序列进行编码。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构建更利于聚类的嵌入空间。为了进一步降低流式场景中的计算开销，我们引入自适应阈值检测（ADATD）。基于六个基线方法和六个数据集的综合实验表明，E2USD在显著降低计算开销的同时能够达到最先进的准确率。我们的代码可在https://github.com/AI4CTS/E2Usd 获取。