Title page for etd-0608115-102943


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URN etd-0608115-102943
Author Tzung-cheng Yang
Author's Email Address ioal123852@hotmail.com
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Department Applied Mathematics
Year 2014
Semester 2
Degree Master
Type of Document
Language English
Title Analysis of Interval Time Series Data
Date of Defense 2015-06-08
Page Count 35
Keyword
  • vector error correction model
  • vector autoregressive model
  • interval stationarity
  • interval time series
  • interval forecasting
  • Abstract Conventional time series methods are developed for analyzing point-valued data. However, in practice there are many interval-valued time series data, which usually contain more information than point-valued data. It is thus important to develop time series modeling and forecasting techniques for interval-valued data.
    In this paper, we introduce concepts of interval stationarity and related interval statistics and investigate methodology for interval time series analysis. We use vector autoregressive (VAR) and vector error correction (VEC) models to build time series models for interval statistics including : medium, radius, upper and lower bounds, and obtain interval forecasts. We compare the forecast performance of the proposed methods with classical filtering technique :
    the exponential smoothing method, and nonparametric technique : the k-Nearest
    Neighbors (k-NN) algorithm. Finally, in the empirical study, we use stock and index data to evaluate the forecast performance and efficiency of the proposed interval time series models.
    Advisory Committee
  • Mong-Na Lo Huang - chair
  • Liang-Ching Lin - co-chair
  • May-Ru Chen - co-chair
  • Shih-Feng Huang - co-chair
  • Mei-Hui Guo - advisor
  • Files
  • etd-0608115-102943.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2015-07-08

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