  Cyber-physical system sensors emit multivariate time series (MTS) that
monitor physical system processes. Such time series generally capture unknown
numbers of states, each with a different duration, that correspond to specific
conditions, e.g., "walking" or "running" in human-activity monitoring.
Unsupervised identification of such states facilitates storage and processing
in subsequent data analyses, as well as enhances result interpretability.
Existing state-detection proposals face three challenges. First, they introduce
substantial computational overhead, rendering them impractical in
resourceconstrained or streaming settings. Second, although state-of-the-art
(SOTA) proposals employ contrastive learning for representation, insufficient
attention to false negatives hampers model convergence and accuracy. Third,
SOTA proposals predominantly only emphasize offline non-streaming deployment,
we highlight an urgent need to optimize online streaming scenarios. 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.
