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This paper explores using a multimodal LLM to generate domain-specific feature pools for time series anomaly detection, showing that a simple statistical pipeline can match the performance of advanced pretrained models on benchmark datasets.
The author critiques current Time Series Anomaly Detection benchmarks, showing that a simple Statistical Process Control algorithm can outperform state-of-the-art methods, suggesting benchmarks are too trivial and calling for more challenging problems in the community.
This paper proposes a zero-shot time-series anomaly detection framework that enhances LLM inputs with frequency-domain evidence from FFT to better capture structural anomalies.
ProtoX-AD is a prototype-based self-explainable framework for self-supervised time series anomaly detection that provides interpretable explanations for detected anomalies by learning transformation-aware prototypes, achieving performance comparable to black-box methods while offering semantic anomaly characterization.