Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2014
Committee Chair or Co-Chairs
Edith Seier
Committee Members
Yali Liu, Robert Price Jr.
Abstract
Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea temperature in a point of the coast of South America, near where “EI Ni˜no” phenomenon originates, and to artificial time series simulated using harmonic functions. The output of the basic SSA method is then compared with that of other decomposition methods such as classic seasonal decomposition, X-11 decomposition using moving averages and seasonal decomposition by Loess (STL) that are included in some time series courses.
Document Type
Thesis - unrestricted
Recommended Citation
Deng, Cheng, "Time Series Decomposition Using Singular Spectrum Analysis" (2014). Electronic Theses and Dissertations. Paper 2352. https://dc.etsu.edu/etd/2352
Copyright
Copyright by the authors.