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A Bayesian time series model with multiple structural change-points for electricity data
Journal of the Korean Data & Information Science Society 2017;28:889-98
Published online July 31, 2017
© 2017 Korean Data & Information Science Society.

Jaehee Kim1

1Department of Statistics, Duksung Women's University
Correspondence to: Jaehee Kim
Professor, Department of Statistics, Duksung Women's University, Seoul, Korea. Email: jaehee@duksung.ac.kr
Received May 31, 2017; Revised July 17, 2017; Accepted July 18, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this research multiple change-points estimation for South Korean electricity gen- eration data is considered. We analyze the South Korean electricity data via determin- istically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to nd the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this opti- mization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River ow data additionally.
Keywords : Bayesian posterior, change-point, genetic algorithm, structural change, time series


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