CARE, a model-agnostic cascaded inference framework that integrates a Lightweight Pre-filter Model (LPM) with an existing high-capacity Complex Detection Model (CDM) and introduces a Structure Attention module to explicitly capture channel-wise anomaly contributions, achieves inference speedup compared to the most accurate SOTA approaches.
Abstract
While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns. To mitigate this bottleneck, we propose CARE, a model-agnostic cascaded inference framework that integrates a Lightweight Pre-filter Model (LPM) with an existing high-capacity Complex Detection Model (CDM). The LPM rapidly filters high-confidence normal samples using a Residual MLP AutoEncoder and a Normality-Conditioned Gating mechanism. Crucially, we introduce a Structure Attention module to explicitly capture channel-wise anomaly contributions, and optimize the gating network via a confidence-guided selective routing objective that learns reliable routing decisions to reduce unnecessary CDM invocations. Extensive experiments across eight real-world benchmarks demonstrate that CARE effectively isolates high-confidence normal samples. By routing only uncertain samples to the CDM, our framework achieves $2.7\times$ to $4.8\times$ inference speedup compared to the most accurate SOTA approaches, while still maintaining competitive detection quality.
LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.
Tu Nguyen, Dang Nguyen, Thuc Duy Le et al.· 0 citations
Anomaly detection for multivariate time series is a critical task with broad applications in industrial monitoring, IT operations, and healthcare. Recent deep learning methods—including reconstruction-based, forecasting-based, and representation-learning approaches—have substantially improved detection accuracy by modeling complex temporal dependencies and inter-variable correlations within a fixed observation window. However, these methods have largely overlooked the historical context preceding the window-of-interest, referred to as the current window. Since time series anomalies are inherently contextual, modeling only the current window while neglecting long-term historical patterns inevitably leads to a high false alarm rate. In this paper, we propose CHAIN (long-Context History-supervised Anomaly detectIoN), a novel framework that explicitly captures long-term historical contexts under anomaly simulation and supervises current-window anomaly detection via masked cross-attention fusion. CHAIN adopts a history–current encoder–decoder design comprising three modules: a history context encoder based on the structured state space sequence model (S4) for efficient long-range modeling; a current context encoder based on a decoder-only Transformer for autoregressive forecasting; and a fused context decoder employing cascaded cross-attention layers that suppress simulated anomalous history via a learned mask. We jointly optimize autoregressive forecasting and a mask-prediction self-supervised auxiliary task in an end-to-end manner. Extensive experiments on three real-world benchmarks (SWaT, WADI, and SMD) demonstrate that CHAIN achieves competitive detection performance against fifteen state-of-the-art baselines. Ablation studies further verify the effectiveness of each proposed component.
Extensive experiments demonstrate that MeCoLog consistently outperforms state-of-the-art baselines under few-shot cross-system settings, offering a robust and highly transferable solution for real-world log anomaly detection.
Manh Tuan Nguyen, Tuan Phong Tran, Le Dinh Trang Dang et al.· PLoS ONE· 0 citations
Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity. Autoencoders are widely used for anomaly detection because they reconstruct normal patterns well, leading to elevated reconstruction errors for anomalous inputs. Their simplicity and efficiency also make them suitable lightweight backbones for handling multi-scale inputs. To address these challenges, we propose a Lightweight MultiScale AutoEncoder (LMSAE) network for univariate time-series anomaly detection, designed to be compact and computationally efficient. LMSAE leverages the Discrete Wavelet Transform (DWT) to extract multi-scale features and employs a multi-scale loss function to improve sensitivity to subtle or hidden anomalies. Experiments on benchmark datasets demonstrate competitive or superior detection performance despite using significantly fewer parameters and a model size of less than 500 KB. LMSAE also achieves low-latency, low-power inference on the NVIDIA Jetson Nano, with 9x reduction in inference latency and 2x reduction in power consumption, making it ideal for edge deployment.
Raheen Junaid Wani, S. Sarangi· arXiv.org· 0 citations
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