PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2009 ◽  
Vol 01 (02) ◽  
pp. 231-242 ◽  
Author(s):  
R. K. NIAZY ◽  
C. F. BECKMANN ◽  
J. M. BRADY ◽  
S. M. SMITH
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Noise Levels ◽  
Stopping Criteria ◽  
Mode Decomposition ◽  
The Common ◽  
Common Problems ◽  
Mode Mixing

Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the effect of selecting different stopping criteria and noise levels. Our results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition.

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Pawel Rzeszucinski ◽  
Michal Juraszek ◽  
James R. Ottewill
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Information ◽  
Optimal Parameters ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Ultrasound Signal ◽  
Diagnostic Capabilities ◽  
Mixing Problem ◽  
Mode Mixing

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

Application of MEEMD-ARIMA combining model for annual runoff prediction in the Lower Yellow River

2019 ◽  
Vol 11 (3) ◽  
pp. 865-876 ◽  
Author(s):  
Xianqi Zhang ◽  
Wei Tuo ◽  
Chao Song
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Yellow River ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Runoff ◽  
Lower Yellow River ◽  
The Lower Yellow River ◽  
Mode Decomposition ◽  
Runoff Prediction

Abstract The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

scholarly journals Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1882 ◽  
Author(s):  
Taiyong Li ◽  
Zhenda Hu ◽  
Yanchi Jia ◽  
Jiang Wu ◽  
Yingrui Zhou
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Bayesian Learning ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Sparse Bayesian Learning ◽  
Crude Oil Prices ◽  
Mode Decomposition ◽  
Model Confidence Set

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

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