@inproceedings{f362e709f7e1420990fd13692c14ac5b,
title = "Online nonlinear granger causality detection by quantized kernel least mean square",
abstract = "Identifying causal relations among simultaneously acquired signals is an important challenging task in time series analysis. The original definition of Granger causality was based on linear models, its application to nonlinear systems may not be appropriate. We consider an extension of Granger causality to nonlinear bivariate time series with the universal approximation capacity in reproducing kernel Hilbert space (RKHS) while preserving the conceptual simplicity of the linear model. In particular, we propose a computationally simple online measure by means of quantized kernel least mean square (QKLMS) to capture instantaneous causal relationships.",
keywords = "Granger causality, Kernel methods, Nonlinear time series, Quantized kernel least mean square(QKLMS)",
author = "Hong Ji and Badong Chen and Zejian Yuan and Nanning Zheng and Andreas Keil and Pr{\'i}ncipe, \{Jose C.\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",
year = "2014",
doi = "10.1007/978-3-319-12640-1\_9",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "68--75",
editor = "Loo, \{Chu Kiong\} and Yap, \{Keem Siah\} and Wong, \{Kok Wai\} and Andrew Teoh and Kaizhu Huang",
booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",
}