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Çѱ¹¼öÀÚ¿øÇÐȸ / v.30, no.6, 1997³â, pp.641-648
½Ã°è¿­ ¼ö¹®ÀÚ·áÀÇ ºñ¼±Çü »ó°ü°ü°è
( How to Measure Nonlinear Dependence in Hydrologic Time Series )
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»ó°ü°è¼ö°¡ º¯¼ö°£ÀÇ ¼±Çü »ó°ü°ü°è¸¦ ³ªÅ¸³»µíÀÌ mutual informationÀº º¯¼ö°£ÀǺñ¼±Çü »ó°ü°ü°è¸¦ ³ªÅ¸³»ÁØ´Ù. º» ³í¹®¿¡¼­´Â mutual information ÃßÁ¤¹ýÀ¸·Î ´Ùº¯¼ö ÇÙ ¹ÌµµÇÔ¼ö(multivariate kernel density estimator)¸¦ ÀÌ¿ëÇÑ ¹æ¹ýÀÌ ¿©·¯ time lags°ª¿¡ ´ëÇÏ¿© »êÁ¤ µÇ¾ú´Ù. ¸¹Àº ¼ö¹®ÀÚ·á¿¡¼­ º¸¿©Áö´Â ºñ¼±Çü °ü°è¸¦ Mutual InformationÀ¸·Î È®ÀÎÇÏ¿© º¸¾Ò°í, ¶ÇÇÑ Mutual Information°ªÀÌ °ÅÀÇ 0ÀÎ Á¡¿¡¼­ optimal delay timeÀ» ±¸ÇÏ¿©, ÇϳªÀÇ ÀÚ·á·ÎºÎÅÍ ´Ùº¯¼ö ȸ±ÍºÐ¼® ¸ðµ¨À» ¸¸µé ¶§ ÀÌ¿ëÇÒ ¼ö ÀÖ´Ù.
Mutual information is useful for analyzing nonlinear dependence in time series in much the same way as correlation is used to characterize linear dependence. We use multivariate kernel density estimators for the estimation of mutual information at different time lags for single and multiple time series. This approach is tested on a variety of hydrologic data sets, and suggested an appropriate delay time $ au$ at which the mutual information is almost zerothen multi-dimensional phase portraits could be constructed from measurements of a single scalar time series.
 
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»ó°ü°è¼ö;Áöü½Ã°£;ÇÙ ¹ÐµµÇÔ¼ö;mutual information;correlation;delay time;kernel density estimators;mutual information;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.30, no.6, 1997³â, pp.641-648
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO199711920100583)
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