IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v291y2021i2p560-574.html
   My bibliography  Save this article

Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)

Author

Listed:
  • Erkuş, Ekin Can
  • Purutçuoğlu, Vilda

Abstract

Outlier detection is one of the main challenges in the pre-processing stage of data analyses. In this study, we suggest a new non-parametric outlier detection technique which is based on the frequency-domain and Fourier Transform definitions and call it as the frequency-domain based outlier detection (FOD). From simulation results under various distributions and real data applications, we observe that our proposal approach is capable of detecting quasi-periodic outliers in time series data more successfully compared with other commonly used methods like z-score, box-plot and also faster than some specialized methods Grubbs method and autonomous anomaly detection (AAD) method. Therefore, we consider that our proposal approach can be an alternative approach to find quasi-periodic outliers in time series data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:291:y:2021:i:2:p:560-574
    DOI: 10.1016/j.ejor.2020.01.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221720300357
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2020.01.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wei Hu & Junpeng Bao, 2013. "The Outlier Interval Detection Algorithms on Astronautical Time Series Data," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, March.
    2. N. Locantore & J. Marron & D. Simpson & N. Tripoli & J. Zhang & K. Cohen & Graciela Boente & Ricardo Fraiman & Babette Brumback & Christophe Croux & Jianqing Fan & Alois Kneip & John Marden & Daniel P, 1999. "Robust principal component analysis for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 1-73, June.
    3. Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008. "Outlier identification in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1694-1711, January.
    4. Nautz, Dieter & Scheithauer, Jan, 2011. "Monetary policy implementation and overnight rate persistence," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1375-1386.
    5. Yufeng Yu & Yuelong Zhu & Shijin Li & Dingsheng Wan, 2014. "Time Series Outlier Detection Based on Sliding Window Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, October.
    6. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    7. Ahmed, T. & Muttaqi, K.M. & Agalgaonkar, A.P., 2012. "Climate change impacts on electricity demand in the State of New South Wales, Australia," Applied Energy, Elsevier, vol. 98(C), pages 376-383.
    8. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinhui Ma & Haijing Huang & Mingxi Peng & Yihuan Zhou, 2024. "Investigating the Heterogeneity Effects of Urban Morphology on Building Energy Consumption from a Spatio-Temporal Perspective Using Old Residential Buildings on a University Campus," Land, MDPI, vol. 13(10), pages 1-24, October.
    2. Nurullah Celal Uslu & Fuat Akal, 2022. "A Machine Learning Approach to Detection of Trade-Based Manipulations in Borsa Istanbul," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 25-45, June.
    3. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
    2. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    3. 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).
    4. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    5. 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.
    6. 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.
    7. Guangxing Wang & Sisheng Liu & Fang Han & Chong‐Zhi Di, 2023. "Robust functional principal component analysis via a functional pairwise spatial sign operator," Biometrics, The International Biometric Society, vol. 79(2), pages 1239-1253, June.
    8. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    9. Italo R. Lima & Guanqun Cao & Nedret Billor, 2019. "M-based simultaneous inference for the mean function of functional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 577-598, June.
    10. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    11. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    12. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    13. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    14. Nuno Cassola & Claudio Morana, 2008. "Modeling Short-Term Interest Rate Spreads in the Euro Money Market," International Journal of Central Banking, International Journal of Central Banking, vol. 4(4), pages 1-37, December.
    15. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    16. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    17. Q. Farooq Akram & Casper Christophersen, 2017. "Pricing in the Norwegian Interbank Market – the Effects of Liquidity and Implicit Government Support," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 165-204, April.
    18. Gissler, Stefan & Oldfather, Jeremy & Ruffino, Doriana, 2016. "Lending on hold: Regulatory uncertainty and bank lending standards," Journal of Monetary Economics, Elsevier, vol. 81(C), pages 89-101.
    19. Alina Evstigneeva & Mark Sidorovskiy, 2021. "Assessment of Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 3-33, September.
    20. Hei-Chia Wang & Tzu-Ting Hsu & Yunita Sari, 2019. "Personal research idea recommendation using research trends and a hierarchical topic model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1385-1406, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:291:y:2021:i:2:p:560-574. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.