Classification of Motor Imagery Tasks using Phase Space Reconstruction and Empirical Mode Decomposition

A hybrid forecasting approach combining empirical mode decomposition (EMD), phase space reconstruction (PSR), and extreme learning machine (ELM) for international uranium resource prices is proposed. In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs), with different frequencies. In the second stage, the IMFs are composed into three subseries based on the fine-to-coarse reconstruction rule. In the third stage, based on phase space reconstruction, different ELM models are used to model and forecast the three subseries, respectively, according to the intrinsic characteristic time scales. Finally, in the foruth stage, these forecasting results are combined to output the ultimate forecasting result. Experimental results from real uranium resource price data demonstrate that the proposed hybrid forecasting method outperforms RBF neural network (RBFNN) and single ELM in terms of RMSE, MAE, and DS.

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Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2016.04.009 ◽

2016 ◽

Vol 19 (3) ◽

pp. 1457-1464 ◽

Cited By ~ 16

Author(s):

Syed Khairul Bashar ◽

Mohammed Imamul Hassan Bhuiyan

Keyword(s):

Fourier Transform ◽

Empirical Mode Decomposition ◽

Motor Imagery ◽

Hybrid Method ◽

Short Time Fourier Transform ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition ◽

Short Time

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Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine

Energies ◽

10.3390/en12020277 ◽

2019 ◽

Vol 12 (2) ◽

pp. 277 ◽

Cited By ~ 10

Author(s):

Wei Sun ◽

Ming Duan

Keyword(s):

Phase Space ◽

Extreme Learning Machine ◽

Empirical Mode Decomposition ◽

Carbon Price ◽

Ensemble Empirical Mode Decomposition ◽

Phase Space Reconstruction ◽

Carbon Markets ◽

Mode Decomposition ◽

Partial Autocorrelation Function ◽

Learning Machine

With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.

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