scholarly journals Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

2021 ◽  
Vol Volume 14 ◽  
pp. 1941-1955
Author(s):  
Jizhen Li ◽  
Yuhong Li ◽  
Ming Ye ◽  
Sanqiao Yao ◽  
Chongchong Yu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Tuberculosis Incidence ◽  
Mode Decomposition

scholarly journals Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongbin Wang ◽  
Chunjie Xu ◽  
Sanqiao Yao ◽  
Lei Wang ◽  
Yingzheng Zhao ◽  
...  
Keyword(s):  
South Africa ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Mortality Data ◽  
Hybrid Technique ◽  
Mode Decomposition ◽  
The Individual

AbstractIn this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model, to perform time series modeling and forecasting based on the COVID-19 prevalence and mortality data from 28 February 2020 to 27 June 2020 in South Africa and Nigeria. By comparing the accuracy level of forecasting measurements with the basic ARIMA and NARANN models, it was shown that this novel data-driven hybrid model did a better job of capturing the dynamic changing trends of the target data than the others used in this work. Our proposed mixture technique can be deemed as a helpful policy-supportive tool to plan and provide medical supplies effectively. The overall confirmed cases and deaths were estimated to reach around 176,570 [95% uncertainty level (UL) 173,607 to 178,476] and 3454 (95% UL 3384 to 3487), respectively, in South Africa, along with 32,136 (95% UL 31,568 to 32,641) and 788 (95% UL 775 to 804) in Nigeria on 12 July 2020 using this data-driven EEMD-ARIMA-NARANN hybrid technique. The contributions of this study include three aspects. First, the proposed hybrid model can better capture the dynamic dependency characteristics compared with the individual models. Second, this new data-driven hybrid model is constructed in a more reasonable way relative to the traditional mixture model. Third, this proposed model may be generalized to estimate the epidemic patterns of COVID-19 in other regions.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

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.

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