A Multivariate Time Series Anomaly Detection Model Based on Spatio-Temporal Dual Features

This paper proposes a novel unsupervised model for multivariate time series anomaly detection (TSAD), targeting the challenges of sparse and unlabeled abnormal data, as well as high dimensionality in IoT applications. The core of our model is to extract spatiotemporal dual features through a coheren...

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Bibliographic Details

Published in:	2023 International Conference on Networking and Network Applications (NaNA) pp. 416 - 421
Main Authors:	Wang, Fangwei, Yan, Man, Li, Qingru, Wang, Changguang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2023
Subjects:	Anomaly detection Computational modeling Data models Deep convolutional autoencoder Feature extraction Stability analysis Time series Time series analysis Transformer Transformer cores Transformers
Online Access:	Get full text
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