IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v60y2023i3d10.1007_s12597-023-00650-3.html
   My bibliography  Save this article

Classical and fast parameters tuning in nearest neighbors with stop condition

Author

Listed:
  • Samya Tajmouati

    (Mohammed V University in Rabat)

  • Bouazza El Wahbi

    (Ibn Tofail University)

  • Mohamed Dakkon

    (Abdelmalek Essaâdi University, FSJES)

Abstract

In this paper, we study the convergence of the algorithms employed by Classical Parameters Tuning in Nearest Neighbors (CPTO-WNN) and Fast Parameters Tuning in Nearest Neighbors (FPTO-WNN). CPTO-WNN and FPTO-WNN are two methodologies that perform the selection of weighted nearest neighbors parameters in time series setting. To do so, each methodology employs an algorithm named WNNoptimization. The WNNoptimization algorithm employs a method inspired by time series cross validation to pick automatically the weighted nearest neighbors parameters. Practically, it computes the global accuracy measure over the test sets, for different values of weighted nearest neighbors parameters and returns the ones for which the accuracy measure is minimal. Compared to CPTO-WNN, FPTO-WNN presents the advantage of reducing time complexity. However, when we are faced with many iterations, both methods raise concerns about computational time. One solution is to introduce a stop condition if the algorithms are convergent. Real data examples on retail and food services sales in the USA and milk production in the UK are analyzed to demonstrate the convergence of the algorithms. As a result, we propose a stop condition allowing the reduction of time complexity while maintaining a good precision. We compare the forecasting performance and time complexity of CPTO-WNN and FPTO-WNN after implementing the proposed stop condition. Experiments show that the introduction of the proposed stop condition provides good accuracy along with lower computational time.

Suggested Citation

  • Samya Tajmouati & Bouazza El Wahbi & Mohamed Dakkon, 2023. "Classical and fast parameters tuning in nearest neighbors with stop condition," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1063-1081, September.
    • Handle: RePEc:spr:opsear:v:60:y:2023:i:3:d:10.1007_s12597-023-00650-3
    DOI: 10.1007/s12597-023-00650-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-023-00650-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12597-023-00650-3?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. Samya Tajmouati & Bouazza El Wahbi & Mohamed Dakkon, 2022. "Modeling COVID-19 Confirmed Cases Using a Hybrid Model," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 128-162, March.
    2. Runze Li & Dennis K.J. Lin & Bing Li, 2013. "Statistical inference in massive data sets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(5), pages 399-409, September.
    3. Zhang, Ningning & Lin, Aijing & Shang, Pengjian, 2017. "Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 161-173.
    4. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    Full references (including those not matched with items on IDEAS)

    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. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    2. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    4. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    5. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. Brummelhuis, Raymond & Luo, Zhongmin, 2019. "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper 94779, University Library of Munich, Germany.
    7. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”," AQR Working Papers 201701, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2017.
    8. Martins, Guilherme Santos & Giesbrecht, Mateus, 2021. "Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 787-805.
    9. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," AQR Working Papers 201802, University of Barcelona, Regional Quantitative Analysis Group, revised Apr 2018.
    11. UÄŸur Åžener & Salvatore Joseph Terregrossa, 2024. "A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts," SAGE Open, , vol. 14(3), pages 21582440241, August.
    12. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
    13. Maheronnaghsh, Mohammad Javad & Gheidi, Mohammad Mahdi & Fazli, MohammadAmin, 2023. "Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price," OSF Preprints dzp26, Center for Open Science.
    14. Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
    15. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    16. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    17. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    18. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    19. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    20. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.

    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:spr:opsear:v:60:y:2023:i:3:d:10.1007_s12597-023-00650-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.