Financial time series are notoriously difficult to analyze and predict, given their non-stationary, highly oscillatory nature. In this study, we evaluate the effectiveness of the Ensemble Empirical Mode Decomposition (EEMD), the ensemble version of Empirical Mode Decomposition (EMD), at generating a representation for market indexes that improves trend prediction. Our results suggest that the p...

Empirical mode decomposition (EMD) is one of the most efficient methods used for nonparametric signal denoising. In this study wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. The principles of hard and soft wavelet thresholding including translation invariant denoising were appropriately modified to develop denoising methods suited for ...

As digital image watermarking has become an important tool for copyright protection, various watermarking schemes have been proposed in literature. Among them image watermarking using bidimensional empirical mode decomposition (EMD) is a newly developed method. In this review paper a comparison of EMD based methods of image watermarking is done. The use of Bidimensional Empirical Mode Decomposi...

The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on...

Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numeric...

In this paper, an alternative optimization based approach to the empirical mode decomposition (EMD) is proposed. The principle is to build first an approximation of the signal mean envelope, which serves as initial guess for the optimization procedure. We develop several optimization strategies to approximate the mean envelope which compare favorably with the original EMD on AM/FM signals.

Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of monoand multivariate signals without any change in the core of the algorithm. Qualit...

Like almost all natural phenomena, speech is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis for nonlinear and nonstationary data. We applied EMD analys...

Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show that the proposed method is superior to the fusion methods based on à-trous wavelet transform (AWT...