@inproceedings{77668d9bf7d0441db78e09d6fee24217,
title = "A novel time-frequency analysis approach for nonstationary time series using multiresolution wavelet",
abstract = "An efficient time-varying autoregressive (TVAR) modeling scheme using the multiresolution wavelet method is proposed for modeling nonstationary signals and with application to time-frequency analysis (TFA) of time-varying signal. In the new parametric modeling framework, the time-dependent parameters of the TVAR model are locally represented using a novel multiresolution wavelet decomposition scheme. The wavelet coefficients are estimated using an effective orthogonal least squares (OLS) algorithm. The resultant estimation of time-dependent spectral density in the signal can simultaneously achieve high resolution in both time and frequency, which is a powerful TFA technique for nonstationary signals. An artificial EEG signal is included to show the effectiveness of the new proposed approach. The experimental results elucidate that the multiresolution wavelet approach is capable of achieving a more accurate time-frequency representation of nonstationary signals.",
keywords = "Chebyshev polynomials, Kalman filter, Time-varying models, multiresolution wavelet, orthogonal least squares (OLS), system identification, time-frequency analysis",
author = "Tan, \{Si Rui\} and Yang Li and Ke Li",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 ; Conference date: 14-12-2014",
year = "2015",
month = jan,
day = "26",
doi = "10.1109/ICDMW.2014.89",
language = "英语",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
number = "January",
pages = "990--995",
editor = "Zhi-Hua Zhou and Wei Wang and Ravi Kumar and Hannu Toivonen and Jian Pei and \{Zhexue Huang\}, Joshua and Xindong Wu",
booktitle = "Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014",
address = "美国",
edition = "January",
}