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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/59440


    Title: 串流資料分析在台灣股市指數期貨之應用
    An Application of Streaming Data Analysis on TAIEX Futures
    Authors: 林宏哲
    Lin, Hong Che
    Contributors: 徐國偉
    Hsu, Kuo Wei
    林宏哲
    Lin, Hong Che
    Keywords: 資料串流探勘
    概念飄移
    台灣股市期貨
    data stream mining
    concept drift
    TAIEX Futures
    Date: 2012
    Issue Date: 2013-09-02 16:48:39 (UTC+8)
    Abstract: 資料串流探勘是一個重要的研究領域，因為在現實中有許多重要的資料以串流的形式產生或被收集，金融市場的資料常常是一種資料串流，而通常這類型資料的本質是變動性大的。在這篇論文中我們運應了資料串流探勘的技術去預測台灣加權指數期貨的漲跌。對機器而言，預測期貨這種資料串流並不容易，而困難度跟概念飄移的種類與程度或頻率有關。概念飄移表示資料的潛在分布改變，這造成預測的準確率會急遽下降，因此我們專注在如何處理概念飄移。首先我們根據實驗的結果推測台灣加權指數期貨可能存在高頻率的概念飄移。另外實驗結果指出，使用偵測概念飄移的演算法可以大幅改善預測的準確率，甚至對於原本表現不好的演算法都能有顯著的改善。在這篇論文中我們亦整理出專門處理各類概念飄移的演算法。此外，我們提出了一個多分類器演算法，有助於偵測「重複發生」類別的概念飄移。該演算法相比改進之前，其最大的特色在於不需要使用者設定每個子分類器的樣本數，而該樣本數是影響演算法的關鍵之一。
    Data stream mining is an important research field, because data is usually generated and collected in a form of a stream in many cases in the real world. Financial market data is such an example. It is intrinsically dynamic and usually generated in a sequential manner. In this thesis, we apply data stream mining techniques to the prediction of Taiwan Stock Exchange Capitalization Weighted Stock Index Futures or TAIEX Futures. Our goal is to predict the rising or falling of the futures. The prediction is difficult and the difficulty is associated with concept drift, which indicates changes in the underlying data distribution. Therefore, we focus on concept drift handling. We first show that concept drift occurs frequently in the TAIEX Futures data by referring to the results from an empirical study. In addition, the results indicate that a concept drift detection method can improve the accuracy of the prediction even when it is used with a data stream mining algorithm that does not perform well. Next, we explore methods that can help us identify the types of concept drift. The experimental results indicate that sudden and reoccurring concept drift exist in the TAIEX Futures data. Moreover, we propose an ensemble based algorithm for reoccurring concept drift. The most characteristic feature of the proposed algorithm is that it can adaptively determine the chunk size, which is an important parameter for other concept drift handling algorithms.
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    Description: 碩士
    國立政治大學
    資訊科學學系
    100753020
    101
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100753020
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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