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Jump point detection using empirical mode decomposition

Real estate is an important form of investment in Hong Kong. Recent researches on the analysis of real estate market have revealed that jump points in the housing price time series play an essential role in the Hong Kong economy. Detecting such jump points thus becomes important as they represent vi...

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Detalles Bibliográficos
Autores principales: Lam, Benson S.Y., Yu, Carisa K.W., Choy, Siu-Kai, Leung, Jacky K.T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115718/
https://www.ncbi.nlm.nih.gov/pubmed/32287824
http://dx.doi.org/10.1016/j.landusepol.2016.07.006
Descripción
Sumario:Real estate is an important form of investment in Hong Kong. Recent researches on the analysis of real estate market have revealed that jump points in the housing price time series play an essential role in the Hong Kong economy. Detecting such jump points thus becomes important as they represent vital findings that enable policy-makers and investors to look forward. In this paper, we propose a jump point detection methodology, which makes use of the empirical mode decomposition algorithm and a derivative-based detector, to detect jump points in the time series of some housing price indices in Hong Kong. Experimental results reveal that our proposed method has a superior performance and outperforms the current state-of-the-art wavelet approach.