IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v3y2018i4p62-d189002.html

A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction

Author

Listed:
  • Alexander Vlasenko

    (Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Nataliia Vlasenko

    (Department of Informatics and Computer Engineering, Faculty of Economic Informatics, Simon Kuznets Kharkiv National University of Economics, 61166 Kharkiv, Ukraine)

  • Olena Vynokurova

    (Information Technology Department, IT Step University, 79019 Lviv Oblast, Ukraine
    Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Dmytro Peleshko

    (Information Technology Department, IT Step University, 79019 Lviv Oblast, Ukraine)

Abstract

Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

Suggested Citation

  • Alexander Vlasenko & Nataliia Vlasenko & Olena Vynokurova & Dmytro Peleshko, 2018. "A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction," Data, MDPI, vol. 3(4), pages 1-14, December.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:62-:d:189002
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/3/4/62/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/3/4/62/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    2. Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
    3. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    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. Alexander Vlasenko & Nataliia Vlasenko & Olena Vynokurova & Yevgeniy Bodyanskiy & Dmytro Peleshko, 2019. "A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting," Data, MDPI, vol. 4(3), pages 1-11, August.

    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. 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.
    2. repec:jss:jstsof:41:i08 is not listed on IDEAS
    3. Alptekin, Aynur & Broadstock, David C. & Chen, Xiaoqi & Wang, Dong, 2019. "Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions," Energy Economics, Elsevier, vol. 82(C), pages 26-41.
    4. Charles Ka Yui Leung & Joe Cho Yiu Ng & Edward Tang, 2020. "Why is the Hong Kong Housing Market Unaffordable? Some Stylized Facts and Estimations," Globalization Institute Working Papers 380, Federal Reserve Bank of Dallas.
    5. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    6. Hammad Mahmoud A. & Jereb Borut & Rosi Bojan & Dragan Dejan, 2020. "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 11(1), pages 51-76, February.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Veenstra, Joost, 2015. "Output growth in German manufacturing, 1907–1936. A reinterpretation of time-series evidence," Explorations in Economic History, Elsevier, vol. 57(C), pages 38-49.
    9. Fei Gu & Kristopher J. Preacher & Emilio Ferrer, 2014. "A State Space Modeling Approach to Mediation Analysis," Journal of Educational and Behavioral Statistics, , vol. 39(2), pages 117-143, April.
    10. Petar Sorić, 2022. "Ability to consume versus willingness to consume: the role of nonlinearities," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(3), pages 663-689, August.
    11. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    12. Bergmann, Dennis & O’Connor, Declan & Thümmel, Andreas, 2013. "A decomposition analysis of the EU farm gate milk price," 87th Annual Conference, April 8-10, 2013, Warwick University, Coventry, UK 158702, Agricultural Economics Society.
    13. Guglielmo Maria Caporale & Abdurrahman Nazif Catik & Gül Serife Huyugüzel Kisla & Mohamad Husam Helmi & Coskun Akdeniz, 2021. "Oil Prices, Exchange Rates and Sectoral Stock Returns in the BRICS-T Countries: A Time-Varying Approach," CESifo Working Paper Series 9322, CESifo.
    14. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    15. Markmann, Holger & Zietz, Joachim, 2017. "Determining the effectiveness of the Eurosystem’s Covered Bond Purchase Programs on secondary markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 314-327.
    16. Michael Jacobs, 2020. "A Holistic Model Validation Framework for Current Expected Credit Loss (CECL) Model Development and Implementation," IJFS, MDPI, vol. 8(2), pages 1-36, May.
    17. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
    18. Zhidan Luo & Wei Guo & Qingfu Liu & Yiuman Tse, 2023. "A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1138-1149, August.
    19. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    20. 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.
    21. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jdataj:v:3:y:2018:i:4:p:62-:d:189002. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.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.