Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
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TLDR
In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
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Short-period solar cycle signals in the ionosphere observed by FORMOSAT-3/COSMIC
TL;DR: In this article, the authors analyzed 2 years of the FORMOSAT-3/COSMIC GPS radio occultation data to study the response of the Earth's ionosphere to the solar rotation induced solar flux variations.
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A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network
TL;DR: Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW), ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARimA model with E MD preprocessing.
Journal ArticleDOI
Effect of walking speed on inter-joint coordination differs between young and elderly adults.
Shiu-Ling Chiu,Li-Shan Chou +1 more
TL;DR: It is suggested that young and elder adults compromise changes of walking speed with different neuromuscular control strategies.
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EEG classification of imagined syllable rhythm using Hilbert spectrum methods.
TL;DR: The results suggest that the rhythmic structure of imagined syllable production can be detected in non-invasive brain recordings and provide a step towards the development of an EEG-based system for communicating imagined speech.
Journal ArticleDOI
MODWT-ARMA model for time series prediction
TL;DR: A hybrid MODWT-ARMA model is presented by combining the maximal overlap discrete wavelet transform (MODWT) and the ARMA model to deal with the non-stationary and LRD time series to improve forecasting accuracy.
References
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TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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