An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework

[1] 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. Search in Google Scholar

[2] 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. Search in Google Scholar

[3] A. Chatterjee, B.S. Ahmed, IoT Anomaly Detection Methods and Applications: A Survey, Internet of Things, 19, 2022, 100568. Search in Google Scholar

[4] 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. Search in Google Scholar

[5] 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. Search in Google Scholar

[6] 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. Search in Google Scholar

[7] A. Singh and K. Chatterjee, Cloud Security Issues and Challenges: A Survey, Journal of Network and Computer Applications, 79, 2017, 88–115. Search in Google Scholar

[8] 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. Search in Google Scholar

[9] V. Barnett and T. Lewis, Outliers in Statistical Data, 3rd ed., Wiley, New York 1994. Search in Google Scholar

[10] 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. Search in Google Scholar

[11] 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. Search in Google Scholar

[12] P. M. Tehrani, Cyber Resilience Strategy and Attribution in the Context of International Law, European Conference on Cyber Warfare and Security, 2019, 501–XVI. Search in Google Scholar

[13] 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. Search in Google Scholar

[14] N. B. Aissa and M. Guerroumi, Semi-Supervised Statistical Approach for Network Anomaly Detection, Procedia Computer Science, 83, 2016, 1090–1095. Search in Google Scholar

[15] 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. Search in Google Scholar

[16] 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. Search in Google Scholar

[17] V. Chandola, A. Banerjee, and V. Kumar, Anomaly Detection: A Survey, ACM Computing Surveys (CSUR), 41(3), 2009, 1–58. Search in Google Scholar

[18] 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. Search in Google Scholar

[19] R.S. Sutton and A.G. Barto, Reinforcement Learning, second edition: An Introduction, MIT Press, 2018. Search in Google Scholar

[20] 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. Search in Google Scholar

[21] 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. Search in Google Scholar

[22] 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. Search in Google Scholar

[23] H. Deng, G. Runger, E. Tuv, and V. Martyanov, A Time Series Forest for Classification and Feature Extraction, Information Sciences, 239, 2013, 142–153. Search in Google Scholar

[24] Lim, Bryan, Zohren, and Stefan, Time-Series Forecasting with Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 2021, 20200209. Search in Google Scholar

[25] M. Braei and S. Wagner, Anomaly Detection in Univariate Time-Series: A Survey on the State-ofthe-Art, arXiv preprint arXiv:2004.00433, 2020. Search in Google Scholar

[26] C.C. Aggarwal, Outlier Analysis, Springer International Publishing, 2016. Search in Google Scholar

[27] N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld, Toward Supervised Anomaly Detection, Journal of Artificial Intelligence Research, 46, 2013, 235–262. Search in Google Scholar

[28] V. N. Vapnik, An Overview of Statistical Learning Theory, IEEE Transactions on Neural Networks, 10(5), 1999, 988–999. Search in Google Scholar

[29] 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. Search in Google Scholar

[30] E. M. Knorr, R. T. Ng, and V. Tucakov, Distance-Based Outliers: Algorithms and Applications, The VLDB Journal, 8(3), 2000, 237–253. Search in Google Scholar

[31] 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. Search in Google Scholar

[32] 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. Search in Google Scholar

[33] 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. Search in Google Scholar

[34] 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. Search in Google Scholar

[35] 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. Search in Google Scholar

[36] 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. Search in Google Scholar

[37] N. Görnitz, M. Braun, and M. Kloft, Hidden Markov Anomaly Detection, International Conference on Machine Learning, 2015, 1833–1842. Search in Google Scholar

[38] 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. Search in Google Scholar

[39] M. Awad, and R. Khanna, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Springer Nature, 2015. Search in Google Scholar

[40] L. M. Manevitz and M. Yousef, One-Class SVMs for Document Classification, Journal of Machine Learning Research, 2(Dec), 2001, 139–154. Search in Google Scholar

[41] 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. Search in Google Scholar

[42] 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. Search in Google Scholar

[43] 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. Search in Google Scholar

[44] 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. Search in Google Scholar

[45] 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. Search in Google Scholar

[46] F. T. Liu, K. M. Ting, and Z.-H. Zhou, Isolation Forest, 2008 Eighth IEEE International Conference on Data Mining, 2008, 413–422. Search in Google Scholar

[47] P. J. Rousseeuw and M. Hubert, Anomaly Detection by Robust Statistics, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), 2018, e1236. Search in Google Scholar

[48] H. Hoffmann, Kernel PCA for Novelty Detection, Pattern Recognition, 40(3), 2007, 863–874. Search in Google Scholar

[49] 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. Search in Google Scholar

[50] 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. Search in Google Scholar

[51] S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, 9(8), 1997, 1735–1780. Search in Google Scholar

[52] 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. Search in Google Scholar

[53] 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. Search in Google Scholar

[54] S. González-Carvajal and E. C. Garrido-Merchán, Comparing BERT Against Traditional Machine Learning Text Classification, arXiv preprint arXiv:2005.13012, 2020. Search in Google Scholar

[55] 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. Search in Google Scholar

[56] M. Dong, H. Huang, and L. Cao, Can LLMs Serve As Time Series Anomaly Detectors?, arXiv preprint arXiv:2408.03475, 2024. Search in Google Scholar

[57] 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. Search in Google Scholar

[58] 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. Search in Google Scholar

[59] 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. Search in Google Scholar

[60] 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. Search in Google Scholar

[61] Y. Zhao, R. Rossi, and L. Akoglu, Automatic Un-supervised Outlier Model Selection, Advances in Neural Information Processing Systems, 34, 2021, 4489–4502. Search in Google Scholar

[62] 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. Search in Google Scholar

[63] A Platform for Open Data of the European Power System, available at https://open-power-system-data.org/, Accessed on: Aug. 9, 2024. Search in Google Scholar

[64] 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. Search in Google Scholar

[65] 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. Search in Google Scholar

[66] M. Galati, USAD: UnSupervised Anomaly Detection on Multivariate Time Series, available at https://github.com/manigalati/usad, 2020. Accessed on: Aug. 9, 2024. Search in Google Scholar

[67] 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. Search in Google Scholar

[68] 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. Search in Google Scholar