Jump point detection using empirical mode decomposition

• Benson S.Y. Lam ^[1] ; Carisa K.W. Yu ^[1] ; Siu-Kai Choy ^[1] ; Jacky K.T. Leung ^[1]
  1. [1] Hang Seng Management College

     Hang Seng Management College

     RAE de Hong Kong (China)

     
• Localización: Land use policy: The International Journal Covering All Aspects of Land Use, ISSN 0264-8377, ISSN-e 1873-5754, Nº. 58, 2016, págs. 1-8
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.