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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/3840


    Title: 混合型時間序列模型之分析
    Other Titles: Analysis of Mixtures of Time Series Models
    Authors: 翁久幸
    Keywords: 時間序列;混合模型;支撐向量法;期望值-最大化
    Time series;Mixture models;Support vector machines;Expectation-maximization
    Date: 2002
    Issue Date: 2007-04-18 16:36:42 (UTC+8)
    Publisher: 臺北市：國立政治大學統計學系
    Abstract: 時間序列分析所討論的課題很多，這裡我們考慮關於時間序列的分段與確認(Segmentation and identification)之問題。也就是說，若一時間序列由若干個未知 模型分別在不同的時間區間內生成，我們要找出其分段點及其生成之模型。我們的解決辦法是結合支撐向量法(Support vector machines)與統計的叢聚分析 (Clustering analysis)。該方法可以應用於許多複雜的時間序列，例如 Mackey-Glass, EEG。本論文創新處包括提出一個支撐向量法的新形式，與一個調整控制模型間競爭程度之參數的新方法。前者主要是對支撐向量法模型的誤差項給予不同的權重，以配合該時間序列是由若干個未知模型分別生成的特質；後者則是利用最大概似估計法調整參數。此研究成果已發表於研討會 (Chang, Lin, and Weng [2])，而此研討會論文經過重新 整理後，已經投稿於IEEE Transactions on Neural Networks，目前已被接受，即將刊登[3]。上述之方法也被應用在 Traveling salesman problems, 並且發表於研討會(Chang, Lin, and Weng [3])。
    We present a framework for the unsupervised segmentation of switching dynamics using support vector machines. Following the architecture by Pawelzik et al. [8] where annealed competing neural networks were used to segment a non-stationary time series, in this article we exploit the use of support vector machines, a well-known learning technique. First, a new formulation of support vector regression is proposed. Second, an expectation-maximization (EM) step is suggested to adaptively adjust the annealing parameter. Experimental results using chaotic time series indicate that the proposed approach is promising.
    Description: 核定金額：406200元
    Data Type: report
    Appears in Collections:[統計學系] 國科會研究計畫

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