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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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…This study proposes a novel hybrid model for water quality prediction. [Methods] First, the original water quality sequence was decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). …”
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Short-term Load Forecasting Based on CNN-LSTM with Quadratic Decomposition Combined by DENG Bowen, XIAO Shenping, LIAO Shiying

“…Firstly, the original load series was decomposed into several intrinsic mode components and residuals by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). …”
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BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization by Danqi Zheng, Jiyun Qin, Zhen Liu, Qinglei Zhang, Jianguo Duan, Ying Zhou

“…First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). …”
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Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model by Na Fang, Zhengguang Liu, Shilei Fan

“…In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. …”
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From Signal to Safety: A Data-Driven Dual Denoising Model for Reliable Assessment of Blasting Vibration Impacts by Miao Sun, Jing Wu, Junkai Yang, Li Wu, Yani Lu, Hang Zhou

“…Firstly, it applies endpoint processing (EP) to the signal, followed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to suppress low-frequency clutter. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed to decompose carbon price data into distinct modal components, each defined by specific frequency characteristics. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm by Yonghui Duan, Chen Li, Xiang Wang, Yibin Guo, Hao Wang

“…The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. …”
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Study on Noise Reduction of Hydrostatic Leveling Signals for Wind Turbine Foundations Based on CEEMDAN-SG Algorithm by Renjie Li, Xiangxing Lu, Zhixin Song, Huanwei Wei, Fang Tan, Zhonghua Liu

“…The hydrostatic leveling monitoring data related to the settlement of the wind turbine foundation display substantial fluctuations along with considerable noise. In this study, based on the characteristic of the hydrostatic level measurement data of wind turbine foundation, a joint denoising method that integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with Savitzky–Golay (SG) filtering is proposed. …”
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Research on de-noising method of optical fiber grating sensing signal based on S-G and CEEMDAN technology by HUANG Gang, PAN Like, HE Chengzhang, YANG Chengfeng, LI Zhong, GONG Shaokang

“…Aiming at the problems of low accuracy and poor reliability of existing signal de-noising methods in the signal processing of fiber Bragg grating sensing system, based on the principle of fiber Bragg grating sensing, a signal de-noising method of fiber Bragg grating sensing system combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and S-G filtering method is proposed. …”
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CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas by Xu Yang, Xinxin Yao, Xinjian Fang, Xuexiang Yu, Yi Wu, Shicheng Xie

“…The results of simulation and example analysis show that compared with wavelet denoising (WD), empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), this method shows significant advantages in the evaluation indexes of noise reduction effect—Pearson’s correlation coefficient (R), the signal-to-noise ratio (SNR), and the root-mean-square error (RMSE)—and all the indexes are better than the comparative methods. …”
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Research on Fault Diagnosis Method of High-Speed EMU Air Compressor Based on ICEEMDAN and Wavelet Threshold Combined Noise Reduction by Liqiang Peng, Akang Guo, Shuzhao Zhang

“…In response to this problem, this paper proposes a high-speed train air compressor fault diagnosis method based on an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). …”
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Mode Shape Extraction with Denoising Techniques Using Residual Responses of Contact Points of Moving Vehicles on a Beam Bridge by Guandong Qiao, Xiaoyue Du, Qi Wang, Liu Jiang

“…A comprehensive investigation is conducted on several critical parameters, including window size, vehicle velocity, road roughness, and beam damping property, as well as the influence of traffic flow. To enhance the mode shape extraction performance using the approximate expression of the contact points’ displacements under noisy disturbance, two new signal denoising methods, CEEMDAN-NSPCA and CEEMDAN-IWT, are proposed based on complete ensemble empirical mode decomposition (CEEMDAN). …”
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Real-time damage detection of bridges using adaptive time-frequency analysis and ANN by V. Ahmadian, S. B. Beheshti Aval, E. Darvishan

“…First, three adaptive signal processing techniques including Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Hilbert Vibration Decomposition (HVD) are compared. …”
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RESEARCH OF EARLY FAULT FEATURE EXTRACTION OF SOLAR WHEEL BASED ON PARAMETRIC ADAPTIVE ICEEMDAN AND MCKD by ZHAO Naizhuo, ZHAO Yumeng, MEN Chengfu

“…In order to solve the problem of difficult to accurately extract early faults of solar wheels under the strong noise background, an improved grey wolf algorithm (newGWO) was proposed to optimize and improve the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the maximum correlated kurtosis deconvolution (MCKD) for early fault feature extraction of solar wheels.NewGWO was used to optimize the selection of parameters of the white noise amplitude weight and noise addition times that affected the decomposition effect.The fault vibration signal was decomposed by newGWO-ICEEMDAN, and the minimum envelope entropy was selected as the fitness function to obtain several related modal components.Then, the envelope spectrum peak factor was selected as the best modal component index.MCKD signals optimized by newGWO were enhanced for the selected optimal intrinsic mode function （IMF） components. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars by Song Zhang, Yuntao Yang, Yilin Xie, Haoran Tang, Haiyang Li, Lianbi Yao, Yin Yang

“…However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. …”
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