Development of novel hybrid models for the prediction of Covid-19 in Kuwait by Ahmad Aldousari, Maria Qurban, Ijaz Hussain, Maha Al-Hajeri

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Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling by WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan

Subjects: “…dam deformation; complete ensemble empirical mode decomposition of adaptive noise; sample entropy reconstruction; k-means clustering algorithm; symbiotic search algorithm; variational mode decomposition…”
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Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory by Lan Cao, Haoyu Yang, Chenggong Zhou, Shaochi Wang, Yingang Shen, Binxia Yuan

“…To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. …”
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Mode Decomposition Bi-Directional Long Short-Term Memory (BiLSTM) Attention Mechanism and Transformer (AMT) Model for Ozone (O₃) Prediction in Johannesburg, South Africa by Israel Edem Agbehadji, Ibidun Christiana Obagbuwa

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Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model by Han Qiu, Rong Hu, Jiaqing Chen, Zihao Yuan

“…To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). …”
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Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear by WANG Shuyu, LI Hao, MA Gang, YUAN Yubo, BU Qiangsheng, YE Zhigang

Subjects: “…improved complete ensemble empirical mode decomposition with adaptive noise|temporal convolutional networks|dlinear|photovoltaic forecast…”
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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

Subjects: “…Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)…”
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Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm by Hua Xu, Zongkai Guo, Yu Cao, Xu Cheng, Qiong Zhang, Dan Chen

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Accurate earthquake and mining tremor identification via a CEEMDAN-LSTM framework by Chao Wang, Xiufeng Zhang, Weiming Gao, Fangfang Wang, Jianqi Lu, Jianqi Lu, Zhaoyang Yan, Zhaoyang Yan

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Financial time series classification method based on low‐frequency approximate representation by Bing Liu, Huanhuan Cheng

Subjects: “…complete ensemble empirical mode decomposition with adaptive noise…”
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Vibration Characteristics of Double-Shield TBM Cutterhead Under Rock–Machine Interaction Excitation by Guang Zhang, Qing Song, Qiuming Gong, Dongxing Liu, Dongwei Li, Minghao Sun

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Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study by Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao, Xiaodong Shen

“…Given the limitations of traditional forecasting models in capturing different frequency components and noise within complex load sequences, this paper proposes a multi-level decomposition forecasting model using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM). …”
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An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting by Yingying He, Likai Zhang, Tengda Guan, Zheyu Zhang

“…To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

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Energy-Efficient Islanding Detection Using CEEMDAN and Neural Network Integration in Photovoltaic Distribution System by Sulayman Kujabi, Emmanuel Asuming Frimpong, Francis Boafo Effah

“…This paper proposes an enhanced islanding detection method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a pattern recognition neural network (PANN). …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…We integrate complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose carbon emission time series into intrinsic mode functions (IMFs) capturing different frequency bands. …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…In this paper, developed the hybrid modeling combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), along with standalone models support vector machine (SVM-linear), and Random Forest (RF), Random Subspace (RS) for accurate prediction of monthly river water level in Sg Muar at Buloh Kasap, Johor station during 2014 to 2023. …”
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Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

“…This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. …”
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Harmonic Detection Algorithm for Traction Power Supply System Based on ICEEMDAN and Teager Energy Operator by XIE Zeen

“…In view that the conventional harmonic detection algorithms cannot support analysis on nonlinear and non-stationary harmonics in the traction power supply systems, this paper proposes a harmonic detection algorithm based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Teager energy operator (TEO). …”
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