Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using K-Means, Isolation Forest, and Local Outlier Factor-Reference-Cited by-同舟云学术

Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using K-Means, Isolation Forest, and Local Outlier Factor

Published:2023-06-06 Issue: Volume: Page:
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Container-title:2023 12th Mediterranean Conference on Embedded Computing (MECO)
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Author:

Petkovski Aleksandar¹,Shehu Visar¹

Affiliation:

1. South East European University,Faculty of Contemporary Sciences and Technologies,Tetovo,North Macedonia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10154895/10154897/10154991.pdf?arnumber=10154991

Reference14 articles.

1. Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment;mathieu;WATER,2017

2. Unsupervised Anomaly Detection Using K-Means, Local Outlier Factor and One Class SVM

3. A Comparative Analysis of Traditional and Deep Learning-Based Anomaly Detection Methods for Streaming Data

4. Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data

5. Anomaly Detection for Raw Water Quality – A Comparative Analysis of the Local Outlier Factor Algorithm and the Random Forest Algorithms

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