A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting by Yue Yu, Pavel Loskot, Wenbin Zhang, Qi Zhang, Yu Gao

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Statistical package for computing precision covariance matrices via modified Cholesky decomposition by Elías D. Niño-Ruiz, Dylan Samuel Cantillo Arrieta, Giuliano Raffaele Frieri Quiroz, Nicolas Quintero Quintero

“…We introduce a statistical package designed to compute precision covariance matrices using modified Cholesky decomposition, tailored for Atmospheric General Circulation Models (AGCMs). …”
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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.…”
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Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction by Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang

“…Abstract To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). …”
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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statistical significance of the D3Net model. …”
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SIP-IFVM: A Time-evolving Coronal Model with an Extended Magnetic Field Decomposition Strategy by Haopeng Wang, Liping Yang, Stefaan Poedts, Andrea Lani, Yuhao Zhou, Yuhang Gao, Luis Linan, Jiakun Lv, Tinatin Baratashvili, Jinhan Guo, Rong Lin, Zhan Su, Caixia Li, Man Zhang, Wenwen Wei, Yun Yang, Yucong Li, Xinyi Ma, Edin Husidic, Hyun-Jin Jeong, Mahdi Najafi-Ziyazi, Juan Wang, Brigitte Schmieder

“…To address this, we propose an extended magnetic field decomposition strategy and successfully implement it in an implicit MHD coronal model. …”
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A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization by Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Junbin Gao, Paul Kwan

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Improved sub-band JND model with textural decomposition and its application in perceptual image coding by Ming-kui ZHENG, Kai-xiong SU, Wei-xing WANG, Cheng-dong LAN, Xiu-zhi YANG

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An Enhanced TimesNet-SARIMA Model for Predicting Outbound Subway Passenger Flow with Decomposition Techniques by Tianzhuo Zuo, Shaohu Tang, Liang Zhang, Hailin Kang, Hongkang Song, Pengyu Li

“…This research introduces a hybrid forecasting approach that combines an enhanced TimesNet model, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Variational Mode Decomposition (VMD) to improve passenger flow prediction. …”
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Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting by Sheng-Tzong Cheng, Ya-Jin Lyu, Yi-Hong Lin

“…This study proposes a linear time series model architecture based on seasonal decomposition. …”
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Rapid Prediction of Ice Accretion on Swept Wings Based on Proper Orthogonal Decomposition and Surrogate Modelling by J. Du, Q. Guo, Y. Yue, Y. Ma, H. Cheng

“…To enable rapid and accurate ice formation predictions on swept wings, this study proposes a prediction methodology integrating proper orthogonal decomposition (POD) and Kriging surrogate modelling. …”
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Calculation Models for Acoustic Analysis of St. Elizabeth of Hungary Church in Jaworzno Szczakowa by Krzysztof KOSAŁA

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Electric vehicle charging load prediction method based on multi-objective modal decomposition and NAHL neural network by GUO Xinzhe, WANG Yeqin, WANG Chao, WU Mingjiang, YANG Yan, ZHANG Chu

“…To improve the accuracy of electric vehicle charging load prediction, a prediction method based on multi-objective variational mode decomposition (VMD) and automatic artificial neural network with an augmented hidden layer (NAHL) is proposed. …”
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Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method by Yukun Bao, Tao Xiong, Zhongyi Hu

“…With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs. …”
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Mul-material decomposition method for sandstone spectral CT images based on I-MultiEncFusion-Net by Yanfang Wu, Yanfang Wu, Yanfang Wu, Ran Zhang, Ran Zhang, Ran Zhang, Huihua Kong, Huihua Kong, Huihua Kong, Ping Chen, Ping Chen, Ping Chen, Yu Zou

“…The non-local feature extraction module constructs non-local neighborhood relationships and employs Euclidean distance metrics to extract global contextual features from images, thereby enhancing the material decomposition process. To further enhance model accuracy, the decoder computes Huber loss between each output and its corresponding label, while simultaneously incorporating correlations of base material images extracted by a High-Resolution Network (HRNet) as an auxiliary loss constraint for material decomposition. …”
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Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction by Jiao Jiao, Longlong Xiao, Chonglei Wang

“…To solve the above problems, this paper proposes a hyperspectral anomaly detection method based on intrinsic image decomposition and background subtraction. …”
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Development of novel hybrid models for the prediction of Covid-19 in Kuwait by Ahmad Aldousari, Maria Qurban, Ijaz Hussain, Maha Al-Hajeri

“…By using a multilayer model with different decomposition techniques, we developed a novel hybrid model for decomposition and prediction of corona cases in Kuwait. …”
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Combining Laplace transform and Adomian decomposition method for solving singular IVPs of Emden-Fowler of partial differential equations by Waleed Al-Hayani, Fatima Yassin

“…In this paper, the time-dependent Emden-Fowler type partial differential equations and wave-type equations with singular behavior at  are analytically solved using the combined Laplace transform and Adomain decomposition method (LT-ADM). To avoid the singularity behavior for both models at , the benefit of this single global technique is used to present a solid framework. …”
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A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine by Zhongliang Lv, Baoping Tang, Yi Zhou, Chuande Zhou

“…A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. …”
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