Multi-Frequency Information Flows between Global Commodities and Uncertainties: Evidence from COVID-19 Pandemic by Emmanuel Asafo-Adjei, Siaw Frimpong, Peterson Owusu Junior, Anokye Mohammed Adam, Ebenezer Boateng, Robert Ofori Abosompim

“…Consequently, we utilise the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Rényi effective transfer entropy techniques to establish the dynamic flow of information. …”
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Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience by Aastha M. Sathe, Supraja R., Aditya Antony Thomas

“…In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. …”
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A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. …”
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CEEMDAN-MRAL Transformer Vibration Signal Fault Diagnosis Method Based on FBG by Hong Jiang, Zhichao Wang, Lina Cui, Yihan Zhao

“…The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. …”
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WEAK FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON PARAMETER OPTIMIZED MOMEDA AND CEEMDAN by HAN XueFei, SHI Zhan, HUA YunSong

“…Aiming at the problem that the fault feature information of rolling bearing is weak under the strong background noise environment,and the single use of the complete ensemble empirical mode decomposition with adaptive noise（ CEEMDAN）method is not effective in extracting the fault feature,a method based on parameter optimized multi-point optimal minimum entropy deconvolution adjusted（ POMOMEDA） and CEEMDAN was proposed. …”
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Fault diagnosis technology for three-level inverter based on ICEEMDAN-FE and SVM by CAO Ruijun, GUO Qiyi

“…In order to improve the accuracy to diagnose complex open-circuit faults for three-level inverters, a new fault diagnosis method of three-level inverters was proposed, combining improved complete ensemble empirical mode decomposition with adaptive noise-fuzzy entropy (ICEEMDAN-FE) and support vector machine (SVM). …”
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ICEEMDAN–VMD denoising method for enhanced magnetic memory detection signal of micro-defects by Shouhong Ji, Jie Yan, Yang Liu, Guojun He

“…During the enhanced magnetic memory detection of micro-defects, many interference signals appear in the detection signal, which makes it difficult to accurately extract the characteristics of the micro-defect signals, significantly affecting detection effectiveness. When improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed independently for signal denoising, the noise and feature signals of the transition components are retained or removed. …”
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A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD by Chenguang Huang, Jianhui Lin, Jianming Ding, Yan Huang

“…A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). …”
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An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings by Yongze Jin, Xubo Yang, Junqi Liu, Yanxi Yang, Xinhong Hei, Anqi Shangguan

“…In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. …”
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Predicting EV battery state of health using long short term degradation feature extraction and FEA TimeMixer by Weijie Tang, Jiayan Chen, Dongjiao Chen

“…Then, the autoencoder is utilized to fuse the features of long-term and short-term SOH degradation trends extracted by empirical degradation models and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to improve the prediction accuracy over different prediction lengths. …”
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Traffic flow prediction based on improved deep extreme learning machine by Xiujuan Tian, Shuaihu Wu, Xue Xing, Huanying Liu, Heyao Gao, Chun Chen

“…Firstly, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (ICEEMDAN) is employed to improve prediction accuracy. …”
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A sentiment-driven three-stage approach for multi-scale carbon price prediction by Yongliang Liu, Chunling Tang, Aiying Zhou, Kai Yang, Huaiyu Yuan

“…This paper proposes a new hybrid model for carbon trading price forecasting. The model fuses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks, and leverages SnowNLP to derive sentiment scores from news text and the Baidu Index. …”
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Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM by Shucheng Lin, Yue Wang, Haocheng Wei, Xiaoyi Wang, Zhong Wang

“…First, using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) extract mode components from crude oil prices. …”
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Fusion of PSO-SVM and ICEEMDAN for high stability GNSS-MR sea level height estimation by Linghuo Jian, Xinpeng Wang, Haining Hao, Hong Wang, Longshan Yang

“…This study proposes a new GNSS-MR sea surface height retrieval method that combines Particle Swarm Optimization (PSO) optimised Support Vector Machine (SVM) with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). …”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and permutation entropy (PE) criterion are combined to decompose and reconstruct the original load data into multiple relatively stationary mode components, which are respectively input into the CNN-BILTSM network for forecasting. …”
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Interval combined prediction of mine tunnel's air volume considering multiple influencing factors. by Zhen Wang, Erkan Topal, Liangshan Shao, Chen Yang

“…These interval numbers are then preprocessed using an Interval-type Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(In-CEEMDAN) to extract the essential features of the data. …”
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion by Chuanjiang Wang, Junhao Ma, Guohui Wei, Xiujuan Sun

“…Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. …”
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Smoothing Photovoltaic Power Fluctuations for Cascade Hydro-PV-Pumped Storage Generation System Based on a Fuzzy CEEMDAN by Lei Chen, Jun Wang, Zhang Sun, Tao Huang, Fan Wu

“…Based on the optimal base power of variable-speed pumped storage station (VSPSS), a smoothing method of fuzzy complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed in this paper. …”
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Based on this，a comprehensive energy system short-term load forecasting model is proposed，which combines complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN），cross optimization algorithm （CSO），long short term memory （LSTM） network，and multi task learning （MTL）. …”
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Advanced gear fault diagnosis in non-stationary conditions with an improved CEEMDAN-wavelet denoising technique by Zakarya Ouelaa, Abderrazek Djebala, Ramdane Younes, Lilia Chaabi, Nouredine Ouelaa, Khaider Bouacha, Ammar Mrabti

“…The proposed method combines the strengths of Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet denoising to enhance defect identification. …”
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