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

“…【Method】The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) process, generating multiple intrinsic mode functions (IMF). …”
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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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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

“…First， it improves the complete ensemble empirical mode decomposition with adaptive noise and ICEEMDAN decomposes multivariate meteorological sequences to reveal their potential features and obtain multidimensional subsequences that make it easier to learn multiscale features. …”
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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

“…To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). …”
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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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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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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

“…This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). …”
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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

“…By identifying short-period surface waves in the given data and utilizing an improved complete ensemble empirical mode decomposition method with adaptive noise (CEEMDAN) in combination with long short-term memory (LSTM) networks, we conduct a discriminative analysis of seismic events in Liaoning, China, and Japan. …”
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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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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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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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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

“…The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. …”
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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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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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A Short-Term Electricity Load Complementary Forecasting Method Based on Bi-Level Decomposition and Complexity Analysis by Xun Dou, Yu He

“…Firstly, a Hodrick Prescott Filter (HP Filter) is used to decompose the electricity data, extracting the trend and periodic components. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to further decompose the periodic components to obtain several IMF components. …”
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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…This study presents a scalable and adaptive approach for modeling complex, nonlinear, and high-dimensional time series, thereby contributing to the enhancement of intelligent forecasting systems in the economic and financial sectors. …”
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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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Coal Price Forecasting Using CEEMDAN Decomposition and IFOA-Optimized LSTM Model by Zhuang Liu, Xiaotuan Li

“…Abstract This study introduces a novel hybrid forecasting model for coking coal prices, integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short-term memory (LSTM) neural networks, enhanced by an improved fruit fly optimization algorithm (IFOA). …”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). …”
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