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Apr 26, 2022 · In this paper, we propose a novel deep learning-based model for anomaly detection of time series. The proposed model consists of three parallel pipelines.
Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many ...
Jun 2, 2017 · In this paper, we propose a novel deep learning-based model for anomaly detection of time series. The proposed model consists of three parallel ...
Feb 17, 2023 · In this paper, we propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN).
Experimental results on a public time series anomaly detection dataset show that we are able to achieve higher comprehensive performance with fewer parameters ...
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring ...
Abstract. Anomaly detection on time series is an important research topic in data mining, which has a wide range of applications in financial markets, ...
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Jul 8, 2024 · In this paper, we propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN).
We propose KBJNet, an innovative model incorporating Transformer and Dilated Temporal Convolutional Network (TCN) techniques to address these obstacles.
Aug 23, 2024 · This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN.