{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T11:33:56Z","timestamp":1782128036403,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of Fujian Provincial Higher Education Reform and Research: Mechanism of Coordinated Development between Applied Universities and Local Economy from the Perspective of Coupling Theory","award":["FGJY202411"],"award-info":[{"award-number":["FGJY202411"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Accurate forecasting in heterogeneous spatiotemporal environments requires models that are both generalizable and interpretable, while also preserving cross-scale symmetry between temporal and spatial patterns. Existing deep learning approaches often struggle with limited adaptability to data-scarce regions and lack transparency in capturing cross-scale causal factors. To address these challenges, we propose a novel framework, Cross-Scale Symmetry-Aware Causal Spatiotemporal Modeling with Adaptive Fusion and Region-Knowledge Transfer, which integrates three key innovations. First, a Dynamic Spatio-Temporal Fusion Framework (DSTFF) leverages frequency-aware temporal transformations and adaptive graph attention to capture complex multi-scale dependencies, ensuring temporal\u2013spatial symmetry in representation learning. Second, a Region-Knowledge Enhanced Transfer Learning (RKETL) mechanism distills knowledge across regions through teacher\u2013student distillation, graph-based embeddings, and meta-learning initialization, thereby maintaining structural symmetry between data-rich and data-scarce regions. Third, a Multi-Granularity Causal Inference Prediction Module (MCIPM) uncovers cross-scale causal structures and supports counterfactual reasoning, providing causal symmetry across daily, weekly, and monthly horizons. Comprehensive experiments on multi-regional logistics datasets from China and the U.S. validate the effectiveness of our approach. Across six diverse Chinese regions, our method consistently outperforms state-of-the-art baselines (e.g., PatchTST, TimesNet, FEDformer), reducing MAE by 18.5% to 27.4%. On the U.S. Freight dataset, our model achieves significant performance gains with stable long-horizon accuracy, confirming its strong cross-domain generalization. Few-shot experiments further demonstrate that with only 5% of training data, our framework surpasses the best baseline trained with 20% data. Robustness analyses under input perturbations and uncertainty quantification show that the model maintains low error variance and produces well-calibrated prediction intervals. Furthermore, interpretability is concretely realized through MCIPM, which visualizes the learned causal graphs and quantifies each regional factor\u2019s contribution to forecasting outcomes. This causal interpretability enables transparent understanding of how temporal spatial dynamics interact across scales, supporting actionable decision-making in logistics management and policy planning. Overall, this work contributes a unified spatiotemporal learning framework that leverages symmetry principles across scales and regions to enhance interpretability, transferability, and forecasting accuracy.<\/jats:p>","DOI":"10.3390\/sym17112001","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T08:50:07Z","timestamp":1763542207000},"page":"2001","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-Scale Symmetry-Aware Causal Spatiotemporal Modeling with Adaptive Fusion and Region-Knowledge Transfer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0005-5952-7725","authenticated-orcid":false,"given":"Xueyu","family":"Xu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Quanzhou University of Information Engineering, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Systems Science, National University of Singapore, Singapore 119615, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ratneswary","family":"Rasiah","sequence":"additional","affiliation":[{"name":"Graduate School of Business, SEGI University, Kuala Lumpur 47810, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-2671-5617","authenticated-orcid":false,"given":"Rongqing","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Monash University, Subang Jaya 47500, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Humanities and Management, Youjiang Medical University for Nationalities, Baise 533000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. 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