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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/66652


    Title: 隨機模式與混沌模式之預測穩健性探討
    Other Titles: Robust forecasting for the stochastic models and chaotic models
    Authors: 吳柏林;劉文卿;陳奕光
    Wu, B. L.;Liou, W. T.;Chen, Y. K.
    Contributors: 應數所;資管系
    Keywords: 混沌模式;預測;隨機模式;穩健性
    Time series;robust;forecasting;neutral networks
    Date: 1992.09
    Issue Date: 2014-06-10 17:58:13 (UTC+8)
    Abstract: 在這篇論文中，我們比較隨機過程與決定性混沌的性質。我們以神經網路當作決定性模式，以AR(1)當作隨機模式，比較兩者逼近與預測能力的。藉七個不同維度(輸入點的個數)共126個神經網路，建立由6個不同相關係數的AR(1)模式所衍生的18個時間數列模擬資料集。對於逼近能力，我們發現神經網路的逼近能力，通常比AR(l)模式來的好。並且共有較高維度的神經網路是優於較低維度的神經網路。對於預測能力，我們發現假如一個AR(l)時間數列資料的相關係數小於0.3或大於0.4，則具有較低維度神經網路的預制能力是優於AR(l)模式；並且對於其它相關係數，具有較高維度的神經網路的預測能力是優於AR(l)模式。試驗之結果，亦支持了神經網路對於預測一般經濟時間數到資料，是較具穩健性。
    In this paper, we compare the properties of stochastic process with deterministic chaos system using the AR(l) model as the comparing model. 126 neural networks are built to approximate chaotic system for the 18 data sets generated by 6 AR( 1) models. From predicting capabilities of the neural networks, we can appropriately deside the dimensions of the chaotic systems. Also, the emperical results show that the forecasting performance derived from neural networks is more robustic than those derived from the AR( 1) model.
    Relation: 中國統計學報, 30(2), 169-189
    Data Type: article
    Appears in Collections:[Department of Statistics] Periodical Articles

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