Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer by Jun He, Zijian Cheng, Zijie Zhong, Lizhuo Liang, Jianhui Ye

“…This paper proposes a hybrid forecasting model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with ModernTCN-Informer. …”
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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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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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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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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

“…Abstract A new hybrid prediction model is proposed for short-term traffic flow, which is based on Deep Extreme Learning Machine improved by Sparrow Search Algorithm (SSA-DELM). Firstly, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (ICEEMDAN) is employed to improve prediction accuracy. …”
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Research on the rapid diagnosis method for hunting of high-speed trains by Wanru Xie, Yixin Zhao, Gang Zhao, Fei Yang, Zilong Wei, Jinzhao Liu

“…Design/methodology/approach – First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. …”
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Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges by Jose Garcia, Luis Rios-Colque, Alvaro Peña, Luis Rojas

“…It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. …”
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Research on the volatility characteristics and evolutionary mechanism of the “Asian premium” for natural gas by Jian Chai, Mingxiao Zhao, Xiaokong Zhang, Na Li, Zhefei Zhang, Zenghui Liu

“…First, this paper used the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) model to decompose and reconstruct natural gas “Asian premium.” …”
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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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A Novel Joint Denoising Strategy for Coherent Doppler Wind Lidar Signals by Yuefeng Zhao, Wenkai Song, Nannan Hu, Xue Zhou, Jiankang Luo, Jinrun Huang, Qianqian Tao

“…This paper proposes a novel joint denoising algorithm based on SVD-ICEEMDAN-SCC-MF to remove noises in CDWL detection. The SVD-ICEEMDAN-SCC-MF consists of singular value decomposition (SVD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Spearman correlation coefficient (SCC), and median filtering (MF). …”
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Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model by Yanhui Liu, Jiulong Wang, Lingyun Song, Yicheng Liu, Liqun Shen

“…This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. …”
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CEEMDAN-Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral-Bevel Gears by Lingli Jiang, Hongchuang Tan, Xuejun Li, Liman Chen, Dalian Yang

“…The vibration signals of spiral-bevel gears are typically quite complicated, as they present both nonlinear and nonstationary characteristics and are interfered with by strong noise. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method has been proven to be an effective method for analyzing this kind of signal. …”
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Long-Term Hourly Ozone Forecasting via Time–Frequency Analysis of ICEEMDAN-Decomposed Components: A 36-Hour Forecast for a Site in Beijing by Taotao Lv, Yulu Yi, Zhuowen Zheng, Jie Yang, Siwei Li

“…However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. …”
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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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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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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

“…Firstly, multiple contributing factors strongly correlated with the short-term load are selected based on the Spearman correlation analysis, the vector autoregressive (VAR) model with multivariate input is derived, and the Levenberg–Marquardt algorithm is introduced to estimate the model parameters. 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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Redefining volatility forecasting in the aerospace and defense sector: application of CEEMDAN-GARCH models by Viviane Naimy, Tatiana Abou Chedid, Omar Abou Saleh, Nicolas Bitar

“…Abstract This study pioneers the integration of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and advanced GARCH models (IGARCH, SGARCH, and GJR-GARCH) to analyze the volatility of aerospace and defense indices across four countries: China, South Korea, France, and the United Kingdom. …”
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Modeling New Nature of Extraction and State Identification of Vibration Shock Signals From Hydroelectric Generating Units Using LCGSA Optimized RBF Combined With CEEMDAN Sample Ent... by Xiang Li, Yun Zeng, Jing Qian, Boyi Xiao, Neng Fei, Fang Dao, Yidong Zou

“…Initially, the Wavelet Transform (WT) algorithm is employed to denoise the raw signal, which is subsequently decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). …”
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