IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v242y2024ics0951832023006816.html
   My bibliography  Save this article

A multi-period-sequential-index combination method for short-term prediction of small sample data

Author

Listed:
  • Jiang, Hongyan
  • Cheng, Feng
  • Wu, Cong
  • Fang, Dianjun
  • Zeng, Yuhai

Abstract

Based on multi-period-sequential-index combination (MPSIC) technology, three forecasting methods (auto-MPSIC, IV- MPSIC, MSEI-MPSIC) were proposed for short-term prediction of small sample data. Natural gas datasets, coal datasets, electricity datasets and atmosphere datasets were separately tested by using MPSIC method, and then impact of weighting factors, forecasting accuracy analysis were carried out for MPSIC method as well as other comparative methods. The results showed that, auto-MPSIC method was partial to use statistical indicators, such as peak-to-peak, average, root mean square, to decrease prediction error, and meanwhile was also inclined to use sequential index at time of ti-1 next to ti to improve prediction accuracy. It was also concluded that: the proposed MPSIC method could achieve higher prediction accuracy compared with other methods; the robustness of auto-MPSIC method was slightly better than that of IV-MPSIC and MSEI-MPSIC under condition of noisy data, which was attributed to an adaptive weight allocation technology considering statistical distribution of forecasting errors.

Suggested Citation

  • Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006816
    DOI: 10.1016/j.ress.2023.109767
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023006816
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109767?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.

    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:reensy:v:242:y:2024:i:c:s0951832023006816. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.