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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/94722
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/94722


    Title: "Spaghetti "主成份分析之延伸-應用於時間相關之區間型台灣股價資料
    An extension of Spaghetti PCA for time dependent interval data
    Authors: 陳品達
    Chen, Pin-Da
    Contributors: 劉惠美
    鄭宗記

    Liu, Huimei
    Cheng, Tsung-Chi

    陳品達
    Chen, Pin-Da
    Keywords: 主成份分析
    區間型資料
    時間相關
    Principal component analysis
    Interval data
    Time dependent
    Date: 2009
    Issue Date: 2016-05-09 11:37:52 (UTC+8)
    Abstract: 摘要
    近幾年發展的區間型態資料之主成份分析,運用在某些領域的資料上尚未成熟,例如股票價格的資料,這些資料是與時間息息相關地,於是有了時間相關的區間資料分析 (Irpino, 2006. Pattern Recognition Letters 27, 504-513)。本文延續這個分析,針對時間相關之區間型台灣股價資料進行研究。Irpino (2006) 的方法只考慮每週的開盤價與收盤價,為了得到更多資訊,我們提出三種方法,第一個方法,將每週的最高價(最低價)納入分析,由兩點的分析變成三點的分析;第二個方法,我們同時考慮最高價與最低價,變成四點的分析,這兩個方法都能得到原始方法不能得到的資訊-公司的穩定度,其中又以第二個方法較為準確;第三種方法引用Irpino (2006) 的建議,我們改變區間的分配,而此方法得到的結果與原
    始的方法差異不大。
    本文分別收集了台灣金融市場三十家半導體與台指五十中的四十七家公司於民國九十七年九月一號到十二月二十六號共十七週的股價資料進行實證分析。以台指五十為例,分析結果顯示編號17的台達電子工業股份有限公司、編號24的鴻海科技集團,這兩家公司的未來被看好;而編號10的聯陽半導體股份有限公司、編號35的統一超商股份有限公司,此兩家公司的未來不被看好,這四家公司在民國九十八年一月五號到一月七號三天的走勢確實是如此!此外,結果顯示
    金融體系的公司比電子體系的公司來得穩定。

    關鍵字:主成份分析,區間型資料,時間相關
    ABSTRACT
    The methods for principal component analysis on interval data have not been ripe yet in some areas, for example, the data of stock prices that are closely related to the time, so the analysis of time dependent interval data was proposed (Irpino, 2006. Pattern Recognition Letters 27, 504-513). In this paper, we apply this approach to the stock prices data in Taiwan. The original “Spaghetti” PCA in Irpino (2006) considered only the starting and the ending prices for each week. In order to get more information we propose three methods. We consider the highest (lowest) price for each week to our analysis in Method 1, and the analysis changes from two points to three points. In Method 2, we consider all information to our analysis which considers four points. These two methods can get more information than the original one. For example, we can get the information of stability degree of the company. For the Method 3, we quote the suggestion from Irpino (2006) to change the distribution of intervals from uniform to beta. However, the result is similar to the original result.
    In our approach, we collect data of stock prices from 37 companies of semiconductor and 47 companies of TSEC Taiwan 50 index in Taiwan financial market during the 17 weeks from September 1 to December 26, 2008. For TSEC Taiwan 50 index, the results of this analysis are that the future trend of Delta (Delta Electronics Incorporation) which numbers 17 and Foxconn (Foxconn Electronics Incorporation) which numbers 24 are optimistic; And ITE (Integrated Technology Express) which numbers 10 and 7-ELEVEn (President Chain Store Corporation) which numbers 35 are not good. In fact, the trends of these four companies are indicated these results during January 5th to 7th. What’s more, the financial companies are steadier than the electronic industry.

    Keywords: Principal component analysis; Interval data; Time dependent
    Reference: References
    [1] Cazes, P., 1997. Extension de l’analyse en composantes principales à des données
    de type intervalle. Revue de Statistique Appliquée, XLV (3), 5-24.
    [2] Chiu, T.C., 2009. A study of Spaghetti PCA for time dependent interval data.
    [3] Diday, E., Lechevallier, Y. & Opitz, O. (eds.), 1996. Ordinal and Symbolic Data
    Analysis. Springer, ISBN 3-540-61081-2; pp. 372, DM 135.00
    [4] Diday, E. & Esposito, F., 2003. An introduction to Symbolic Data Analysis and
    the SODAS software. Intelligent Data Analysis, 7(6), 583–602, IOS Press.
    [5] D’Urso, P. & Giordani, P., 2004. A least squares approach to principal
    component analysis for interval valued data. Giordani / Chemometrics and
    Intelligent Laboratory Systems, 70, 179–192.
    [6] Goupil, F., Touati, M., Diday, E. & Van Der Veen, H., 2000. Symbolic Analysis
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    [7] Gioia, F. & Lauro, N.C., 2005. Basic statistical methods for interval data.
    Statistica Applicata [Italian Journal of Applied Statistics] 17(1): 75-104.
    [8] Gioia, F. & Lauro, N.C., 2006. Principal component analysis on interval
    data. Computational Statistics, Volume 21, Issue 2. Pages: 343 –363.
    [9] Irpino, A., 2006. “Spaghetti” PCA analysis: An extension of principal components
    analysis to time dependent interval data. Pattern Recognition Letters 27,
    504-513
    [10] Lauro, N.C. & Palumbo, F., 2000. Principal components analysis of interval data
    : A symbolic data analysis approach. Comput. Stat. 15 (1), 79-87.
    [11] Lauro, N.C. & Palumbo, F., 2003. A PCA for interval-valued data based on
    midpoints and radii. In: New developments in Psychometrics, Yanai H., Okada A., Shigemasu K.,Kano Y. and Meulman J., eds., Psychometric Society,
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    [12] Lauro, C.N. & Palumbo, F., 2003. Some results and new perspectives in
    Principal Component Analysis for interval data. 237–244. Cladag Book Short
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    [13] Zuccolotto, P., 2007. Principal components of sample estimates: an approach
    through symbolic data analysis. Stat Meth & Appl, 16: 173–192
    Description: 碩士
    國立政治大學
    統計學系
    96354017
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0096354017
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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