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：我们提出了E2USD，它能够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）和分解式双视图嵌入模块（DDEM），两者以较低的计算开销共同编码输入的多变量时间序列。此外，我们提出了一种假阴性抵消对比学习方法（FNCCLearning）来抵消假阴性的影响，从而实现更有利于聚类的嵌入空间。为了进一步降低流式环境中的计算开销，我们引入了自适应阈值检测（ADATD）。基于六个基线和六个数据集的全面实验表明，E2USD能够在显著降低计算开销的同时达到最先进的精度。我们的代码可在https://github.com/AI4CTS/E2Usd获取。