STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. Therefore, this study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components and graph structures to model both temporal patterns and asset correlations. …”
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New multifactor spatial prediction method based on Bayesian maximum entropy by YANG Yong, ZHANG Chutian, HE Liyuan

“…Currently, the spatial distribution of soil properties is usually predicted with classical geostatistics or environmental correlation. …”
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Enhancing land use planning through integrating landscape analysis and flood inundation prediction Bekasi City’s in 2030 by Novia Fitriyati, Hadi Susilo Arifin, R. L. Kaswanto, Marimin

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A reliability model to predict failure behaviour of overlying strata in groundwater-rich coal mine by Ruirui Li, Xiaowei Hou, Luwang Chen, Yingxin Wang, Fuyou Huang, Lanting Wang

“…In this study, a reliability model with consideration of spatial variability and uncertainty of strength parameters was proposed to predict the failure behaviour of overlying strata during coal mining in groundwater-rich coalfields. …”
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Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model by Yilin Chen, Haitao Zheng

Subjects: “…house price prediction…”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

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Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation. by Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost

“…In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. …”
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From Domain Decomposition to Model Reduction for Large Nonlinear Structures by Leturcq, Bertrand, Le Tallec, Patrick

“…The numerical simulation of multiscale and multiphysics problems requires efficient tools for spatial localization and model reduction. A general strategy combining Domain Decomposition and Nonuniform Transformation Field Analysis (NTFA) is proposed herein for the simulation of nuclear fuel assemblies at the scale of a full nuclear reactor. …”
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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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Modeling robustness tradeoffs in yeast cell polarization induced by spatial gradients. by Ching-Shan Chou, Qing Nie, Tau-Mu Yi

“…In this work, we investigated the tradeoffs among these performance objectives using a generic model that captures the basic spatial dynamics of polarization in yeast cells, which are small. …”
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An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle by Zhi Zhou, Xueling Wu, Bo Peng

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Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
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Modeling the Effects of Spatial Heterogeneity and Seasonality on Guinea Worm Disease Transmission by Anthony A. E. Losio, Steady Mushayabasa

“…The model incorporates seasonal variations, educational campaigns, and spatial heterogeneity. …”
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Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic by Eleftheria Koutsaki, George Vardakis, Nikos Papadakis

“…Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL.…”
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Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response by Yuanyuan Wu, Youyang Shi, Zhanyang Luo, Xiqiu Zhou, Yonghao Chen, Xiaoyun Song, Sheng Liu

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Digital agriculture drives carbon emission reduction in China by Yao Wenyan, Li Chenghan, Lin Fangzhou

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Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches by Salman Selmy, Salah Abd El-Aziz, Ahmed El-Desoky, Moatez El-Sayed

“…The current study was undertaken in the Gharb El-Mawhoub area of Dakhla Oasis to determine, predict, map, and assess the spatial variation of physicochemical attributes. …”
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Modelling the Spatial Dependence of Multi‐Species Point Patterns by Chathuri L. Samarasekara, Ian Flint, Yan Wang

“…ABSTRACT The study of the spatial point patterns in ecology, such as the records of the observed locations of trees, shrubs, nests, burrows, or documented animal presence, relies on multivariate point process models. …”
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