IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v41y2022i7p1512-1524.html
   My bibliography  Save this article

A weights direct determination neuronet for time‐series with applications in the industrial indices of the Federal Reserve Bank of St. Louis

Author

Listed:
  • Spyridon D. Mourtas

Abstract

The shortcomings of conventional back‐propagation neuronets, such as slow training speed and local minimum, are known to be addressed by neuronets trained under the weights‐and‐structure‐determination (WASD) algorithm. Derived from power activation feed‐forward neuronets, a multi‐input WASD for time‐series neuronet (MI‐WASDTSN) model is presented in this paper. The MI‐WASDTSN is equipped with a novel WASD for time‐series (WASDTS) algorithm, for handling time‐series modeling and forecasting problems. Employing a power sigmoid activation function, the WASDTS algorithm handles the model fitting and validation by determining the optimal input variables number and the weights of the MI‐WASDTSN. More specifically, the WASDTS algorithm finds and holds only the activation function powers that reduce the model's error during validation. Applications on Federal Reserve Bank of St. Louis (FRED) industrial indices under three different patterns of time‐series validate our MI‐WASDTSN model in order to demonstrate its outstanding learning and forecasting performance. In addition, to support and advance the findings of this work, we created a MATLAB repository for interested users, which is freely available via GitHub.

Suggested Citation

  • Spyridon D. Mourtas, 2022. "A weights direct determination neuronet for time‐series with applications in the industrial indices of the Federal Reserve Bank of St. Louis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1512-1524, November.
  • Handle: RePEc:wly:jforec:v:41:y:2022:i:7:p:1512-1524
    DOI: 10.1002/for.2874
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2874
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2874?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
    ---><---

    Citations

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


    Cited by:

    1. Dimitris Lagios & Spyridon D. Mourtas & Panagiotis Zervas & Giannis Tzimas, 2023. "A Weights Direct Determination Neural Network for International Standard Classification of Occupations," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    2. Stanimirović, Predrag S. & Mourtas, Spyridon D. & Mosić, Dijana & Katsikis, Vasilios N. & Cao, Xinwei & Li, Shuai, 2024. "Zeroing neural network approaches for computing time-varying minimal rank outer inverse," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    3. Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    4. Rabeh Abbassi & Houssem Jerbi & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking," Mathematics, MDPI, vol. 11(12), pages 1-21, June.

    More about this item

    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:wly:jforec:v:41:y:2022:i:7:p:1512-1524. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.