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
Citations
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Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals
TL;DR: A final comparison between the results obtained with the developed technique and results adopted by Polat and coworkers using Fourier analysis with the same database is given to show the effectiveness of this technique for seizure detection.
Journal ArticleDOI
Random noise attenuation using f-x regularized nonstationary autoregression
TL;DR: In this article, a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space (f-x) domain was developed.
Journal ArticleDOI
Instantaneous frequency in time–frequency analysis: Enhanced concepts and performance of estimation algorithms
Ljubisa Stankovic,Igor Djurovic,Srdjan Stankovic,Marko Simeunovic,Slobodan Djukanovic,Milos Dakovic +5 more
TL;DR: Some of the most important developments in the last two decades related to the concept of the IF, performance analysis ofIF estimators, and development of IF estimators for low SNR environments are reviewed.
Journal ArticleDOI
Daily air quality index forecasting with hybrid models: A case in China
TL;DR: The AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet- GRNN andWavelet-SVR, and can be used as effective and simple tools for air pollution forecasting and warning as well as for management.
Journal ArticleDOI
Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction
Ming-De Liu,Lin Ding,Yulong Bai +2 more
TL;DR: The results in this paper show that the EMD method can improve the wind speed prediction performance when it is combined with LSTM and after decomposition, L STM is suitable for predicting high complexity subsequences and the ARIMA is suited for effectively predicting low complexity subsequence based on the different sample entropies.
References
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