IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/526806.html
   My bibliography  Save this article

Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

Author

Listed:
  • Che-Jung Chang
  • Der-Chiang Li
  • Wen-Li Dai
  • Chien-Chih Chen

Abstract

The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

Suggested Citation

  • Che-Jung Chang & Der-Chiang Li & Wen-Li Dai & Chien-Chih Chen, 2013. "Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, July.
    • Handle: RePEc:hin:jnlmpe:526806
    DOI: 10.1155/2013/526806
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2013/526806.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2013/526806.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/526806?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. Wang, Zhanwei & Song, Woon-Kyung, 2020. "Sustainable airport development with performance evaluation forecasts: A case study of 12 Asian airports," Journal of Air Transport Management, Elsevier, vol. 89(C).
    2. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.

    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:hin:jnlmpe:526806. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.