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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, revised May 2024.
  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. Borges, Dérick G. F. & Coutinho, Eluã R. & Cerqueira-Silva, Thiago & Grave, Malú & Vasconcelos, Adriano O. & Landau, Luiz & Coutinho, Alvaro L. G. A. & Ramos, Pablo Ivan P. & Barral-Netto, Manoel & Pi, 2025. "Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records," LSE Research Online Documents on Economics 127964, London School of Economics and Political Science, LSE Library.
  20. 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).
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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).
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. Timothy DeLise, 2023. "Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market," Papers 2309.00088, arXiv.org.
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