Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/75025


Title: Change periods detection for multivariate time series with fuzzy methods
Authors: Li, W.;Hu, R.;Wu, Berlin
吳柏林
Contributors: 應數系
Keywords: Change periods;Change-points;Cluster centers;Empirical studies;Fuzzy entropy;Fuzzy methods;Fuzzy statistic;Fuzzy time series;Germany;Integrated identification;Macroeconomic indicators;Multivariate time series;Performance indices;Structure change;Testing method;Artificial intelligence;Computer software;Membership functions;Time series;Time series analysis
Date: 2009-12
Issue Date: 2015-05-07 15:28:43 (UTC+8)
Abstract: Researchers have proposed a lot of detecting and testing methods about change points. While in the real case, it shows that the structure change of a time series was changed gradually, that is the change points has illustrated senses of fuzziness. This concept is important in fitting different models to different regimes of the data regarding economic interpretation of the data during that regime. In this paper we present an integrated identification procedure for change periods detection. The membership function of each system, which include multivariate time series data, corresponding to the cluster centers as performance index grouping is calculated. A fuzzy time series C* t is defined on averages of cumulative fuzzy entropies of the three time series. Finally, an empirical study about change periods identification for Germany, France and Greece major macroeconomic indicators are demonstrated. ©2009 IEEE.
Relation: Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009,-5363912
Data Type: conference
DOI 連結: http://dx.doi.org/10.1109/CISE.2009.5363912
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