ReFuzzTiD: A Recurrent Neurofuzzy Model for Anomaly Detection in Time Series

被引：0

作者：

Kandilogiannakis, George ^{[1
]}

Mastorocostas, Paris ^{[1
]}

机构：

[1] Univ West Attica, Dept Informat & Comp Engn, Egaleo, Greece

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

关键词：

time series anomaly detection; neurofuzzy model; recurrent neural network; internal feedback; NEURAL-NETWORKS; FUZZY; SYSTEM;

D O I：

10.1109/ijcnn48605.2020.9206821

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a recurrent neurofuzzy model is proposed, for time series anomaly detection. ReFuzzTiD comprises fuzzy rules whose consequent parts are simple three- layer neural networks with internal feedback at the neurons of the hidden layer. ReFuzzTid is trained by the Simulated Annealing Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis with a series of time series anomaly detection models is given, highlighting the characteristics of the proposed detector.

引用

页数：7

共 50 条

[1] An anomaly detection model for multivariate time series with anomaly perception
Wei, Dong
Sun, Wu
Zou, Xiaofeng
Ma, Dan
Xu, Huarong
Chen, Panfeng
Yang, Chaoshu
Chen, Mei
Li, Hui
[J]. PEERJ COMPUTER SCIENCE, 2024, 10
[2] An anomaly detection model for multivariate time series with anomaly perception
Wei, Dong
Sun, Wu
Zou, Xiaofeng
Ma, Dan
Xu, Huarong
Chen, Panfeng
Yang, Chaoshu
Chen, Mei
Li, Hui
[J]. PeerJ Computer Science, 2024, 10
[3] Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
Yin, Chunyong
Zhang, Sun
Wang, Jin
Xiong, Neal N.
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (01): : 112 - 122
[4] Clustering model for anomaly detection in time series sets
Leng, Mingwei
Lai, Xinsheng
Liu, Yingdong
[J]. Journal of Information and Computational Science, 2010, 7 (01): : 85 - 93
[5] Time series prediction and anomaly detection with recurrent spiking neural networks
Cherdo, Yann
Miramond, Benoit
Pegatoquet, Alain
[J]. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
[6] A Generative Model for Anomaly Detection in Time Series Data
Hoh, Maximilian
Schoettl, Alfred
Schaub, Henry
Wenninger, Franz
[J]. 3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING, 2022, 200 : 629 - 637
[7] Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders
Radaideh, Majdi I.
Pappas, Chris
Walden, Jared
Lu, Dan
Vidyaratne, Lasitha
Britton, Thomas
Rajput, Kishansingh
Schram, Malachi
Cousineau, Sarah
[J]. DIGITAL SIGNAL PROCESSING, 2022, 130
[8] Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders
Kieu, Tung
Yang, Bin
Guo, Chenjuan
Cirstea, Razvan-Gabriel
Zhao, Yan
Song, Yale
Jensen, Christian S.
[J]. 2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1342 - 1354
[9] Model-centered Ensemble for Anomaly Detection in Time Series
Trentini, Erick L.
Coelho da Silva, Ticiana L.
Melo Junior, Leopoldo
de Macedo, Jose F.
[J]. ICAART: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2, 2020, : 700 - 707
[10] Anomaly detection via a combination model in time series data
Yanjun Zhou
Huorong Ren
Zhiwu Li
Naiqi Wu
Abdulrahman M. Al-Ahmari
[J]. Applied Intelligence, 2021, 51 : 4874 - 4887

← 1 2 3 4 5 →