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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

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  1. Ruhi Kiran Bajaj & Rebecca Mary Meiring & Fernando Beltran, 2023. "Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study," Future Internet, MDPI, vol. 15(3), pages 1-15, March.
  2. Davide Nicola Continanza & Andrea del Monaco & Marco di Lucido & Daniele Figoli & Pasquale Maddaloni & Filippo Quarta & Giuseppe Turturiello, 2023. "Stacking machine learning models for anomaly detection: comparing AnaCredit to other banking data sets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
  3. Durgesh Samariya & Amit Thakkar, 2023. "A Comprehensive Survey of Anomaly Detection Algorithms," Annals of Data Science, Springer, vol. 10(3), pages 829-850, June.
  4. VANHOEYVELD, Jellis & MARTENS, David, 2018. "Towards a scalable anomaly detection with pseudo-optimal hyperparameters," Working Papers 2018012, University of Antwerp, Faculty of Business and Economics.
  5. Taha Yehia & Ali Wahba & Sondos Mostafa & Omar Mahmoud, 2022. "Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis," Energies, MDPI, vol. 15(23), pages 1-25, November.
  6. Ke Lu & Xianwen Fang & Na Fang, 2022. "PN-BBN: A Petri Net-Based Bayesian Network for Anomalous Behavior Detection," Mathematics, MDPI, vol. 10(20), pages 1-24, October.
  7. Vinicius Francisco Rofatto & Marcelo Tomio Matsuoka & Ivandro Klein & Maurício Roberto Veronez & Luiz Gonzaga da Silveira Junior, 2020. "On the effects of hard and soft equality constraints in the iterative outlier elimination procedure," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-29, August.
  8. Cian Ryan & Finbarr Murphy & Martin Mullins, 2019. "Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1125-1140, May.
  9. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
  10. Jakob Trauer & Simon Pfingstl & Markus Finsterer & Markus Zimmermann, 2021. "Improving Production Efficiency with a Digital Twin Based on Anomaly Detection," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
  11. Shuo Xu & Liyuan Hao & Xin An & Dongsheng Zhai & Hongshen Pang, 2019. "Types of DOI errors of cited references in Web of Science with a cleaning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1427-1437, September.
  12. Elmira Asadi-Fard & Samereh Falahatkar & Mahdi Tanha Ziyarati & Xiaodong Zhang & Mariapia Faruolo, 2023. "Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
  13. Bruno Faria & Fernao Vistulo de Abreu, 2019. "Cellular frustration algorithms for anomaly detection applications," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-31, July.
  14. Priyanga Dilini Talagala & Rob J Hyndman & Kate Smith-Miles, 2019. "Anomaly Detection in High Dimensional Data," Monash Econometrics and Business Statistics Working Papers 20/19, Monash University, Department of Econometrics and Business Statistics.
  15. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org.
  16. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
  17. Milan Miric & Hakan Ozalp & Erdem Dogukan Yilmaz, 2023. "Trade‐offs to using standardized tools: Innovation enablers or creativity constraints?," Strategic Management Journal, Wiley Blackwell, vol. 44(4), pages 909-942, April.
  18. Parminder Singh & Sujatha Krishnamoorthy & Anand Nayyar & Ashish Kr Luhach & Avinash Kaur, 2019. "Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
  19. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  20. Asoke K Nandi & Kuldeep Kaur Randhawa & Hong Siang Chua & Manjeevan Seera & Chee Peng Lim, 2022. "Credit card fraud detection using a hierarchical behavior-knowledge space model," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
  21. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
  22. Francisco Melo Pereira & Rute C. Sofia, 2022. "An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories," Future Internet, MDPI, vol. 15(1), pages 1-19, December.
  23. Irene Epifanio & Vicent Gimeno & Ximo Gual-Arnau & M. Victoria Ibáñez-Gual, 2020. "A New Geometric Metric in the Shape and Size Space of Curves in R n," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
  24. Zhang, Guangyue & Atasoy, Hilal & Vasarhelyi, Miklos A., 2022. "Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
  25. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
  26. Priyanga Dilini Talagala & Rob J Hyndman & Catherine Leigh & Kerrie Mengersen & Kate Smith-Miles, 2019. "A Feature-Based Framework for Detecting Technical Outliers in Water-Quality Data from In Situ Sensors," Monash Econometrics and Business Statistics Working Papers 1/19, Monash University, Department of Econometrics and Business Statistics.
  27. Wordliczek Lukasz, 2021. "Between incrementalism and punctuated equilibrium: the case of budget in Poland, 1995–2018," Central European Journal of Public Policy, Sciendo, vol. 15(2), pages 14-30, December.
  28. Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
  29. Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
  30. Kenichiro Nagata & Toshikazu Tsuji & Kimitaka Suetsugu & Kayoko Muraoka & Hiroyuki Watanabe & Akiko Kanaya & Nobuaki Egashira & Ichiro Ieiri, 2021. "Detection of overdose and underdose prescriptions—An unsupervised machine learning approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
  31. Sevvandi Kandanaarachchi & Rob J Hyndman, 2019. "Dimension Reduction For Outlier Detection Using DOBIN," Monash Econometrics and Business Statistics Working Papers 17/19, Monash University, Department of Econometrics and Business Statistics.
  32. Timothy DeLise, 2023. "Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market," Papers 2309.00088, arXiv.org.
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