References
- J. E. Zhang, D. Wu, and B. Boulet, Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection, 2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2022, 193–199.
- A.N. Alkuwari, S. Al-Kuwari, M. Qaraqe, Anomaly Detection in Smart Grids: A Survey from Cybersecurity Perspective, 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE), 2022, 1-7.
- A. Chatterjee, B.S. Ahmed, IoT Anomaly Detection Methods and Applications: A Survey, Internet of Things, 19, 2022, 100568.
- A.A. Khalil, F. E Ibrahim, M.Y. Abbass, N. Haggag, Y. Mahrous, A. Sedik, Z. Elsherbeeny, A.M. Khalaf, M. Rihan, W. El-Shafai, Efficient Anomaly Detection from Medical Signals and Images with Convolutional Neural Networks for Internet of Medical Things (IoMT) Systems, International Journal for Numerical Methods in Biomedical Engineering, 38(1), 2022, e3530.
- W. Hilal, S.A. Gadsden, J. Yawney, Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances, Expert Systems with Applications, 193, 2022, 116429.
- M. U. Hassan, M. H. Rehmani, and J. Chen, Anomaly Detection in Blockchain Networks: A Comprehensive Survey, IEEE Communications Surveys & Tutorials, 25(1), 2022, 289–318.
- A. Singh and K. Chatterjee, Cloud Security Issues and Challenges: A Survey, Journal of Network and Computer Applications, 79, 2017, 88–115.
- D. Jung, N. Ramanan, M. Amjadi, S. R. Karingula, J. Taylor, and C. N. Coelho Jr, Time Series Anomaly Detection with Label-Free Model Selection, arXiv preprint arXiv:2106.07473, 2021.
- V. Barnett and T. Lewis, Outliers in Statistical Data, 3rd ed., Wiley, New York 1994.
- L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller, A Unifying Review of Deep and Shallow Anomaly Detection, Proceedings of the IEEE, 109(5), 2021, 756–795.
- M. Gunduz and A. M. A. Yahya, Analysis of Project Success Factors in Construction Industry, Technological and Economic Development of Economy, 24(1), 2018, 67–80.
- P. M. Tehrani, Cyber Resilience Strategy and Attribution in the Context of International Law, European Conference on Cyber Warfare and Security, 2019, 501–XVI.
- J. Ghanim, M. Issa, and M. Awad, An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction, 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022, 819–824.
- N. B. Aissa and M. Guerroumi, Semi-Supervised Statistical Approach for Network Anomaly Detection, Procedia Computer Science, 83, 2016, 1090–1095.
- S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon, Ganomaly: Semi-Supervised Anomaly Detection via Adversarial Training, Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14, 2019, 622–637.
- S. Han, X. Hu, H. Huang, M. Jiang, and Y. Zhao, Adbench: Anomaly Detection Benchmark, Advances in Neural Information Processing Systems, 35, 2022, 32142–32159.
- V. Chandola, A. Banerjee, and V. Kumar, Anomaly Detection: A Survey, ACM Computing Surveys (CSUR), 41(3), 2009, 1–58.
- J. P. S. Chhabra and G. P. Warn, A Method for Model Selection Using Reinforcement Learning When Viewing Design as a Sequential Decision Process, Structural and Multidisciplinary Optimization, 59, 2019, 1521–1542.
- R.S. Sutton and A.G. Barto, Reinforcement Learning, second edition: An Introduction, MIT Press, 2018.
- V. Kosana, K. Teeparthi, S. Madasthu, and S. Kumar, A Novel Reinforced Online Model Selection Using Q-learning Technique for Wind Speed Prediction, Sustainable Energy Technologies and Assessments, 49, 2022, 101780.
- Y. Fu, D. Wu, and B. Boulet, Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 2022, 6639–6647.
- K. Christophe, J. El Zini, and M. Awad, A Progressive and Cross-Domain Deep Transfer Learning Framework for Wrist Fracture Detection, Journal of Artificial Intelligence and Soft Computing Research, 12(2), 2021, 101-120.
- H. Deng, G. Runger, E. Tuv, and V. Martyanov, A Time Series Forest for Classification and Feature Extraction, Information Sciences, 239, 2013, 142–153.
- Lim, Bryan, Zohren, and Stefan, Time-Series Forecasting with Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 2021, 20200209.
- M. Braei and S. Wagner, Anomaly Detection in Univariate Time-Series: A Survey on the State-ofthe-Art, arXiv preprint arXiv:2004.00433, 2020.
- C.C. Aggarwal, Outlier Analysis, Springer International Publishing, 2016.
- N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld, Toward Supervised Anomaly Detection, Journal of Artificial Intelligence Research, 46, 2013, 235–262.
- V. N. Vapnik, An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks, 10(5), 1999, 988–999.
- V. Sindhwani, P. Niyogi, and M. Belkin, Beyond the Point Cloud: From Transductive to Semi-Supervised Learning, Proceedings of the 22nd International Conference on Machine Learning, 2005, 824–831.
- E. M. Knorr, R. T. Ng, and V. Tucakov, Distance-Based Outliers: Algorithms and Applications, The VLDB Journal, 8(3), 2000, 237–253.
- S. Ramaswamy, R. Rastogi, and K. Shim, Efficient Algorithms for Mining Outliers from Large Data Sets, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, 427–438.
- F. Angiulli and C. Pizzuti, Fast Outlier Detection in High Dimensional Spaces, European Conference on Principles of Data Mining and Knowledge Discovery, 2002, 15–27.
- M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying Density-Based Local Outliers, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, 93–104.
- G. O. Campos, A. Zimek, J. Sander, R. J. G. B. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle, On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study, Data Mining and Knowledge Discovery, 30, 2016, 891–927.
- L. Xiong, X. Chen, and J. Schneider, Direct Robust Matrix Factorization for Anomaly Detection, 2011 IEEE 11th International Conference on Data Mining, 2011, 844–853.
- L. Li, J. McCann, N. S. Pollard, and C. Faloutsos, Dynammo: Mining and Summarization of Co-evolving Sequences with Missing Values, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, 507–516.
- N. Görnitz, M. Braun, and M. Kloft, Hidden Markov Anomaly Detection, International Conference on Machine Learning, 2015, 1833–1842.
- A. P. Dawid and A. M. Skene, Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm, Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 1979, 20–28.
- M. Awad, and R. Khanna, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Springer Nature, 2015.
- L. M. Manevitz and M. Yousef, One-Class SVMs for Document Classification, Journal of Machine Learning Research, 2(Dec), 2001, 139–154.
- B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, 13(7), 2001, 1443–1471.
- Z. Li, Y. Zhao, X. Hu, N. Botta, C. Ionescu, and G. H. Chen, ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions, IEEE Transactions on Knowledge and Data Engineering, 35(12), 2022, 12181–12193.
- Z. Li, Y. Zhao, N. Botta, C. Ionescu, and X. Hu, COPOD: Copula-Based Outlier Detection, 2020 IEEE International Conference on Data Mining (ICDM), 2020, 1118–1123.
- A. Kharitonov, A. Nahhas, M. Pohl, and K. Turowski, Comparative Analysis of Machine Learning Models for Anomaly Detection in Manufacturing, Procedia Computer Science, 200, 2022, 1288–1297.
- Z. Xu, D. Kakde, and A. Chaudhuri, Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection, 2019 IEEE International Conference on Big Data (Big Data), 2019, 4201–4207.
- F. T. Liu, K. M. Ting, and Z.-H. Zhou, Isolation Forest, 2008 Eighth IEEE International Conference on Data Mining, 2008, 413–422.
- P. J. Rousseeuw and M. Hubert, Anomaly Detection by Robust Statistics, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), 2018, e1236.
- H. Hoffmann, Kernel PCA for Novelty Detection, Pattern Recognition, 40(3), 2007, 863–874.
- P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection, arXiv preprint arXiv:1607.00148, 2016.
- Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, 2828–2837.
- S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, 9(8), 1997, 1735–1780.
- J. El Zini, Y. Rizk, and M. Awad, An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 11(1), 2021, 33-50.
- J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga, USAD: Unsupervised Anomaly Detection on Multivariate Time Series, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, 3395–3404.
- S. González-Carvajal and E. C. Garrido-Merchán, Comparing BERT Against Traditional Machine Learning Text Classification, arXiv preprint arXiv:2005.13012, 2020.
- W. Dang, B. Zhou, W. Zhang, and S. Hu, Time Series Anomaly Detection Based on Language Model, Proceedings of the Eleventh ACM International Conference on Future Energy Systems, 2020, 544–547.
- M. Dong, H. Huang, and L. Cao, Can LLMs Serve As Time Series Anomaly Detectors?, arXiv preprint arXiv:2408.03475, 2024.
- J. Su, C. Jiang, X. Jin, Y. Qiao, T. Xiao, H. Ma, R. Wei, Z. Jing, J. Xu, and J. Lin, Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review, arXiv preprint arXiv:2402.10350, 2024.
- Y. Li, Z. Chen, D. Zha, K. Zhou, H. Jin, H. Chen, and X. Hu, AUTOOD: Neural Architecture Search for Outlier Detection, 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2021, 2117–2122.
- K.-H. Lai, D. Zha, G. Wang, J. Xu, Y. Zhao, D. Kumar, Y. Chen, P. Zumkhawaka, M. Wan, D. Martinez, et al., TODS: An Automated Time Series Outlier Detection System, Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 2021, 16060–16062.
- Y. Li, D. Zha, P. Venugopal, N. Zou, and X. Hu, PYODDS: An End-to-End Outlier Detection System with Automated Machine Learning, Companion Proceedings of the Web Conference 2020, 2020, 153–157.
- Y. Zhao, R. Rossi, and L. Akoglu, Automatic Un-supervised Outlier Model Selection, Advances in Neural Information Processing Systems, 34, 2021, 4489–4502.
- M. Gulati and P. Arjunan, LEAD1.0: A Large-Scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings, Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, 2022, 485–488.
- A Platform for Open Data of the European Power System, available at https://open-power-system-data.org/, Accessed on: Aug. 9, 2024.
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., Scikit-Learn: Machine Learning in Python, The Journal of Machine Learning Research, 12, 2011, 2825–2830.
- Y. Zhao, Z. Nasrullah, and Z. Li, PYOD: A Python Toolbox for Scalable Outlier Detection, Journal of Machine Learning Research, 20(96), 2019, 1–7.
- M. Galati, USAD: UnSupervised Anomaly Detection on Multivariate Time Series, available at https://github.com/manigalati/usad, 2020. Accessed on: Aug. 9, 2024.
- J. Bergstra, B. Komer, C. Eliasmith, D. Yamins, and D. D. Cox, Hyperopt: A Python Library for Model Selection and Hyperparameter Optimization, Computational Science & Discovery, 8(1), 2015, 014008.
- A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, Stable-Baselines3: Reliable Reinforcement Learning Implementations, Journal of Machine Learning Research, 22(268), 2021, 1–8.