E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
WWW 2024(2024)
摘要
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.
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