Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM. by Xiangjuan Liu, Yunlong Li, Fengtong Wang, Yujie Qin, Zhongyu Lyu

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …”
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A robust deep learning approach for photovoltaic power forecasting based on feature selection and variational mode decomposition by Mokhtar Ali, Abdelkerim Souahlia, Abdelhalim Rabehi, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem

“…Among the selected predictors, Global Solar Radiation (GSR) consistently proves to be the most influential. To further enhance model inputs, Variational Mode Decomposition (VMD) is applied to extract informative Intrinsic Mode Functions (IMFs) from the selected features. …”
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A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams by Jianhua Wang, Bang Ji, Feng Lin, Shilei Lu, Yubin Lan, Lianglun Cheng

“…The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.…”
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Singular Value Decomposition-Based Adaptive Sampling Approximate Message Passing Net for Sparse-View CT Reconstruction by Zhenhua Wu, Jiafei Xu, Lixia Yang

“…However, the challenges of handling a reduced number of projection views persist for both iterative estimation and deep neural reconstruction methods. In this paper, to address these challenges, we present a singular value decomposition-based adaptive sampling approximate message passing network (ASAMP-Net) sparse-view CT imaging method. …”
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Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model by Heng Zhao, Yanyan Zhang, Dengxin Hua, Jiamin Fang, Jie Zhang, Zewen Yang

“…Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. …”
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A Novel Hybrid Approach to Forecasting Crude Oil Prices Using Local Mean Decomposition, ARIMA, and XGBoost by Jawaria Nasir, Muhammad Aamir, Soofia Iftikhar, A. B. Albidah, Ohud A. Alqasem, Maysaa E. A. Elwahab, Ilyas Khan, Wei Sin Koh

“…The hybrid LMD-SD-ARIMA-XGBOOST model overcomes overfitting by combining statistical and machine learning components, where the decomposition process isolates key features of the data, reducing noise and complexity. …”
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Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting. by Fen Xiao, Siyu Yang, Xiao Li, Junhong Ni

“…To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. …”
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AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting by Shikang Hou, Song Sun, Tao Yin, Zhibin Zhang, Meng Yan

“…Furthermore, the intricate structure of multi-scale time series complicates the effective extraction of features at different temporal resolutions.MethodTo address these limitations, we propose AMDCnet, a multi-scale-based time series decomposition and collaboration network designed to enhance the model's capacity for decomposing and integrating data across varying time scales. …”
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A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis by Qi Zhang, Tian Tian, Guangrui Wen, Zhifen Zhang

“…This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. …”
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Colored linear inverse model: A data-driven method for studying dynamical systems with temporally correlated stochasticity by Justin Lien, Yan-Ning Kuo, Hiroyasu Ando, Shoichiro Kido

“…In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The linear inverse model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. …”
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Battery Cluster Inconsistency Detection Method and Intelligent O&M Scheme Based on Vector Error Correction Model by Yuan GUO, Xiangyang XIA, Jiahui YUE, Hui LI, Jinbo WU

“…To solve the problems of incomplete battery data and fragmented data segments leading to inaccurate detection in the actual operation data of energy storage power stations, this paper proposes a battery cluster inconsistency detection method based on the vector error correction model. …”
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Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data by Aihua Zhu, Haote Zhang, Xingqian Chen, Dingkun Zhu

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Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach by Fudong Xie, Ce Wang, Housheng Duan

“…To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. …”
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Ultra-Short-Term Photovoltaic Power Interval Forecasting Based on Time-Series Decomposition and Conformal Quantile Regression by Qianjin GUI, Wenfa XU, Xiaoyang LI, Lirong LUO, Haifeng YE, Zhengfeng WANG

“…Then, piecewise linear models, Fourier series decomposition models, and AR-Net models are respectively employed to fit the three subseries, with the Fourier series decomposition model enhancing the fitting capability for daily and seasonal periodicities of PV power. …”
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification. by Jaipriya D, Sriharipriya K C

“…In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. …”
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Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition by Cai Lu, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, Jiandong Liang

“…The proposed dual-branch PIRNN framework was validated through full-waveform inversions on synthetic horizontal-layer models and the Marmousi model across various scales. …”
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Enhancing artificial neural network learning efficiency through Singular value decomposition for solving partial differential equations by Alfi Bella Kurniati, Maharani A. Bakar, Nur Fadhilah Ibrahim, Hanani Farhah Harun

“…In response, we introduce the matrix decomposition method into the ANN learning process, rooted in Singular Value Decomposition (SVD). …”
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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.…”
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A Rapid Computational Method for Quantifying Inter-Regional Air Pollutant Transport Dynamics by Luoqi Yang, Guangjie Wang, YeGui Wang, Yibai Wang, Yongjing Ma, Xi Zhang

“…The model integrates high-resolution numerical simulations, Geographic Information System (GIS) capabilities, and advanced statistical evaluation metrics with boundary pixel decomposition methods to effectively characterize complex pollutant transport dynamics while ensuring computational efficiency. …”
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Experience in modeling inclined cracks in materials with cubic crystal structure by Karina A. Mushankova, Larisa V. Stepanova

“…The atomic stress distributions associated with the crack tip are obtained using the molecular dynamics method. Continuum distributions are obtained from the theoretical solution of the problem of determining the stress-strain state at the crack tip, based on the methods of the elasticity theory of anisotropic media and the subsequent decomposition of complex potentials by eigenfunctions. …”
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