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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/53431

    Title: 以資料為中心之類神經網路方法論與離群值
    Other Titles: Neural-Networks-Related Data-Based Approach and Outliers
    Authors: 蔡瑞煌
    Contributors: 國立政治大學資訊管理學系
    Keywords: 資料;類神經網路;方法論;離群值
    rare events;stock market;single-hidden layer feed-forward neural networks;the resistant learning context
    Date: 2011
    Issue Date: 2012-08-30 15:49:06 (UTC+8)
    Abstract: 股市生產著大量的日數據,而其輸入輸出關係通常是非線性的、且未知的。但在每日的 股市指數,也有罕見事件(即遠離主體數據點之離群值)。當我們希望通過直接使用收 集到的股市指數,並且採用類神經網路,來進行系統建模、優化和控制時,要從每日的 股市指數裡,系統性地確認罕見事件仍是一問題;而要有效地處置罕見事件則是另外一 問題。 (Tsaih and Cheng, 2009)採用單一隱藏層倒傳遞神經網路(SLFN)為建模工具來說明 以下之穩健學習情境:  在學習過程中,SLFN 的隱藏節點數量是可調整的  在學習過程中,離群值是遠離所得之 SLFN 函數的數據點  潛在的離群值不該在學習的過程中顯著地影響 SLFN 的隱藏節點數量 (Tsaih and Cheng 2009)的穩健學習過程可以把數據分成主體數據點群和潛在的離群值 群。 目前沒有任何研究於穩健學習情境下之有效的以資料為中心之類神經網路方法論 的事實激發了本研究。具體來說,本研究的目標是: (1)設立股市實驗,以探索在穩健學習情境下之有效的神經網絡相關的數據為基礎的方 法。更具體地說,在數據預處理階段,採用(Tsaih and Cheng 2009)的穩健學習過程 來把訓練數據分成主體數據點群和潛在的罕見事件群。在建模階段,採用(Rumelhart, Hinton and Williams 1986)的學習算法調整SLFN 模型。在預測階段,應用取得之 SLFN 模型於預測數據,以獲得相應的預測。 (2)探索精選的罕見事件的相關議題。也就是說,我們要探索以下一些問題:精選的罕 見事件是否是真實的罕見事件?能否有任何進一步的分析這些精選的罕見事件,來 幫助理解如何檢測他們以及他們對股市的影響? (3)驗證在數據預處理階段,採用(Tsaih and Cheng 2009)的穩健學習過程的必要性。
    In modern stock markets whose input-output relationship is normally non-linear and unknown, vast amounts of data are being produced each day. But there are rare events (outliers that are far from the bulk of observations) in the daily index of stock market. When we adopt the Neural-Networks-related (NN-related) data-based approach to perform the system optimization, control and modeling via directly using the input-output data collected from the stock market, to systematically identify the rare event from the daily index of stock market is a study issue and to effectively cope with the outlier is another study issue. With single-hidden layer feed-forward neural networks (SLFN) as the modeling tool, Tsaih and Cheng (2009) describe the resistant learning context as follows:  the number of adopted hidden nodes of SLFN is adaptable within the learning process,  outliers are the observations far away from the (fitting) function defined by the obtained SLFN, and  potential outliers should not impact significantly the number of adopted hidden nodes within the learning process. Furthermore, their resistant learning procedure can separate the data into two subsets, the bulk and the potential outlier. The fact that there is no study of data-based approach in the resistant learning context motivates this study. Specifically, the objectives of this study are: (1) Set up a stock-market experiment to explore an effective NN-related data-based approach for coping with rare events in the resistant learning context. More specifically, in the data-preprocessing stage, the resistant learning procedure of (Tsaih & Cheng, 2009) is adopted to separate the training data into two subsets, the bulk and the (potential) rare events. In the modeling stage, the bulk data is used to tune the SLFN model via the back propagation learning algorithm (Rumelhart, Hinton and Williams 1986). In the forecasting stage, the obtained SLFN model is applied to the forecasting data to get corresponding predictions, based upon which investment decisions are made. (2) Explore the reality of “picked” rare events in the stock market. That is, we want to explore some of the following questions: Are some of the “picked” rare events real ones of stock market? Any further analysis of them can help understand the way of detecting them and the way they affect the stock market? (3) Justify the necessity of data preprocessing mechanism for coping with rare events.
    Relation: 應用研究
    研究期間:10008~ 10107
    Data Type: report
    Appears in Collections:[資訊管理學系] 國科會研究計畫

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