Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…[Methods] This study first employed Multivariate Variational Mode Decomposition(MVMD) to decompose the original daily runoff data from the two stations, reducing data complexity. …”
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Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data by Conor T. Doherty, Meagan S. Mauter

“…This article explores the use of Fisher discriminant analysis (FDA) as a method for extracting time-resolved information from multivariate environmental time series data. …”
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Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model by Chaoyue Ding, Yuqing Sun, Kunchi Li, Sangma Xie, Hao Yan, Peng Li, Jun Yan, Jun Chen, Huiling Wang, Huaning Wang, Yunchun Chen, Yongfeng Yang, Luxian Lv, Hongxing Zhang, Lin Lu, Dai Zhang, Yaojing Chen, Zhanjun Zhang, Tianzi Jiang, Bing Liu

“…In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. …”
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Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. by Kalidou Moussa Sow, Nadia Ghazzali

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A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model by Akram Seifi, Sharareh Pourebrahim, Mohammad Ehteram, Hanieh Shabanian

“…Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. …”
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Developing a reliable predictive model for the biodegradability index in industrial complex effluent by Sadegh Partani, Amin Arzhangi, Hamidreza Azari, Hamidreza Moheghi

“…The extracted model applied on some of the mentioned countries’ records and the results of BOD prediction was matched by observations in 95% of reliability domain. …”
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A Hierarchical Archimedean Copula (HAC) model for climatic variables: an application to Kenyan data by Kevin Otieno, Linda Chaba, Linda Chaba, Evans Omondi, Evans Omondi, Collins Odhiambo, Collins Odhiambo, Bernard Omolo, Bernard Omolo

“…The uniform variates were extracted using the inverse transformation technique. …”
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Application of multivariate principal component analysis to morphological characterization of indigenous goats in Southern Nigeria by Moses OKPEKU, Abdulmojeed YAKUBU, Sunday Olusola PETERS, Michael Ohiokhuaobo OZOJE, Christian Obiora Ndubuisi IKEOBI, Olufunmilayo Ayoka ADEBAMBO, Ikhide Godwin IMUMORIN

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Analytical Techniques for the Authenticity Evaluation of Chokeberry, Blackberry and Raspberry Fruit Wines: Exploring FT-MIR Analysis and Chemometrics by Ivana Vladimira Petric, Boris Duralija, Renata Leder

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Spatial variation of intimate partner violence and its public health implication: a cross-sectional study by Abisola Esther Babatope, Demilade Olusola Ibirongbe, Idowu Peter Adewumi, Damola Olanipekun Ajisafe, Oluwafunbi Ajoke Fadipe, Gbenga Omotade Popoola, Kayode Olayiwola Adepoju, Oluyemi Adewole Okunlola

“…The spatial analysis done on the prevalence of intimate partner violence found a variation in the occurrence of intimate partner violence across the States of the country, while the probability of intimate partner violence obtained from the model ranges from 0.24 to 0.85. …”
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Sustainable Soil Volatilome: Discrimination of Land Uses Through GC-MS-Identified Volatile Organic Compounds by Emoke Dalma Kovacs, Teodor Rusu, Melinda Haydee Kovacs

“…A multivariate statistical method was used to differentiate the volatilome profile. …”
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Factors affecting the readiness of digital transformation adopters: A case study in Vietnam by Vinh T. Nguyen, Hue T. Lai, Quynh V. Ha

“…The results of multivariable regression analysis demonstrated that all extracted factors had an important influence on the readiness of students to transform digitally, in which “behavioral intention” played the most important role. …”
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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

“…Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. …”
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Time Series Decomposition Using Wavelet and Fourier Transforms for Enhanced Solar Flare Forecasting by Victor Solomon, Junzhi Wen, Rafal Angryk, Manya Rampuria, Omkar Rayala, Abdul Afrid

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A Typical Infrared Background Radiation Prediction Model Based on RF-VMD and Optimized Hybrid Neural Network by Bentian Hao, Weidong Xu, Xin Yang, Feifei Xiao, Hao Li, Wei Huang

“…Based on the superimposed IMFs as inputs, a hybrid deep neural network prediction model is established. The model optimizes the CNN-LSTM network with residual connections and introduces a multi-head self-attention mechanism to enhance spatiotemporal feature extraction of the multidimensional meteorological parameters, focusing on key temporal feature regions. …”
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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. …”
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MSPT: A Transformer-Based Model Using Multiscale Periodic Information for 10–30 d Subseasonal Daily Sea Surface Temperature Forecasting by Qi He, Zhenfeng Lan, Wei Song, Wenbo Zhang, Yanling Du, Wei Zhao

“…Furthermore, by introducing additional multivariate attention, our improved Transformer encoder can capture the inherent multivariate correlations of SST dynamics, perfecting the representation of temporal variations at specific periodic scales. …”
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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…Extensive experiments on two real-world datasets demonstrate that, compared to advanced methods that lack sufficient multivariate heterogeneous feature extraction and do not account for traffic emergencies, the STFDSGCN model improves the average absolute error (MAE), root mean square error (RMSE), and average absolute percentage error (MAPE) by 4.01%, 1.33%, and 1.03%, respectively, achieving superior performance.…”
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The ratio of perirenal fat thickness to renal parenchymal thickness, a novel indicator of fat accumulation associated with kidney stones by Dekai Hu, Guoxiang Li, Defeng Ge, Leilei Ke, Hongmin Shu, Yang Chen, Zongyao Hao

“…Subsequent to adjustments for several confounding variables, the multivariable logistic regression model demonstrated a significant correlation between the ratio of perirenal fat thickness to renal parenchymal thickness and stone bearing kidney (P < 0.001). …”
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Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy. by Pascal Salazar, Patrick Cheung, Balaji Ganeshan, Anastasia Oikonomou

“…<h4>Results</h4>Using both Kaplan-Meier analysis with its log-rank tests and multivariate Cox regression analysis, the best radiomic features of both methods were selected: CTTA-based "entropy" and the FPCA-based first mode of variation of tumoural CT density histogram: "F1." …”
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