The Multi-Dimensional Ensemble Empirical Mode Decomposition Method

By Zhaohua Wu, Norden E. Huang, Xianyao Chen. Advances in Data Science and Adaptive Analysis, 2009

390 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/e8ca53210e71ee30984f014f2fc2018a9f41486a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Naveed Urr Rehman
2. Danilo P. Mandic
3. Norden E. Huang
4. Zhaohua Wu
5. Neeraj Dhanraj Bokde
6. Men-Tzung Lo
7. Kun Hu
8. Chien-Hung Yeh

In Electrical and Electronic Engineering

1. Naveed Urr Rehman
2. Danilo P. Mandic
3. Norden E. Huang
4. Zhaohua Wu
5. Gorm Bruun Andresen
6. Patrick Flandrin
7. Gabriel Rilling
8. Tracy Kijewski-Correa

In Nuclear and High Energy Physics

1. Zhaohua Wu
2. Naveed Urr Rehman
3. Danilo P. Mandic
4. Norden E. Huang

Citations in the corpus

• Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013, methodology): “D IMFs are obtained by recombining the x- and y-scales; the MEEMD also scales to volumes of any dimension.”
• Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling (2020, background): “The effect of white noise can be minimized based on the following equation [28] and [30].”
• On the computational complexity of the empirical mode decomposition algorithm (2014, methodology): “Themulti-dimensional ensemble empirical mode decomposition (MEEMD) [10] is themulti-dimension version of EEMD.”