English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 110180/141113 (78%)
Visitors : 46572233      Online Users : 185
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 >  Item 140.119/76737


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/76737


    Title: A study of improving the coherence in multi-step ahead forecasting
    Authors: Hong, C.-F.;Liao, Y.-S.;Lin, Mu-Hua;Hong, T.-H.
    Contributors: 資管系
    Keywords: Prediction errors;Propagation errors;Sequential algorithm;Artificial intelligence;Error analysis;Evolutionary algorithms;Fractals;Parallel algorithms;Forecasting
    Date: 2006
    Issue Date: 2015-07-21 15:05:41 (UTC+8)
    Abstract: The traditional multi-step ahead prediction is based on sequential algorithm to run multi-step ahead prediction and it brings error propagation problem. Furthermore, the prediction error of multi-step ahead includes both system and propagation errors. Therefore, how to decrease the propagation error has become an important issue in multi-step ahead prediction. In this study we had used the parallel algorithm to avoid the propagation error, but it brought a new problem: the incoherent learning method was used to learn the coherent time series, then, it brought an incoherent problem. Therefore, we proposed a novel parallel algorithm: after parallel algorithm, the system had to run the sequential algorithm again to avoid the incoherent problem. The experimental results evidence that the prediction error of the novel parallel algorithm was smaller than that of the parallel algorithm and the prediction error of multi-step ahead was the same as that of one-step ahead. These results imply that the prediction error of the novel parallel algorithm was approaching the system error. In addition the fractal based GP was used to learn the predicting function. The prediction error was as the radius of the trajectory line. Because the fractal was drawn by the pipe line, it indicates that the stock`s price time series belonged to the non-determinate chaos.
    Relation: Proceedings of the 9th Joint Conference on Information Sciences, JCIS 2006, Volume 2006, 論文編號 CIEF-191
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.2991/jcis.2006.152
    DOI: 10.2991/jcis.2006.152
    Appears in Collections:[資訊管理學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML2495View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback