An anomaly detection approach based on the combination of LSTM autoencoder and isolation forest for multivariate time series data
Author:
Affiliation:
1. HEC Liège - Management School of the University of Liège, Liège, 4000, Belgium
2. GEMTEX Laboratory, Ecole Nationale Supérieure des Arts et Industries Textiles, Roubaix, 59560, France
Publisher
WORLD SCIENTIFIC
Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. LSTM-AE for Anomaly Detection on Multivariate Telemetry Data;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23
2. LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data;IEEE Sensors Journal;2023-02-15
3. A Deep Learning-Based Data-Driven Approach for Predicting Mining Water Inrush From Coal Seam Floor Using Microseismic Monitoring Data;IEEE Transactions on Geoscience and Remote Sensing;2023
4. A Novel Autoencoder Deep Architecture for Detecting the Outlier in Heterogeneous Data Sets;Advances in Data Science and Adaptive Analysis;2022-07
5. Applying Anomaly Detection Models in Wastewater Management: A Case Study of Nitrates Concentration in the Effluent;Advances in Artificial Intelligence – IBERAMIA 2022;2022
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