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Surv."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making through anticipatory insights. By accurately predicting future outcomes, the ability to strategize, preemptively address risks, and minimize their potential impact is enhanced. The precision in forecasting spatial and temporal patterns holds significant potential for optimizing resource allocation, land utilization, and infrastructure development. While existing review and survey papers predominantly focus on specific forecasting domains such as intelligent transportation, urban planning, pandemics, disease prediction, climate and weather forecasting, environmental data prediction, and agricultural yield projection, limited attention has been devoted to comprehensive surveys encompassing multiple objects concurrently. This article addresses this gap by comprehensively analyzing techniques employed in traffic, pandemics, disease forecasting, climate and weather prediction, agricultural yield estimation, and environmental data prediction. Furthermore, it elucidates challenges inherent in spatio-temporal forecasting and outlines potential avenues for future research exploration.<\/jats:p>","DOI":"10.1145\/3696661","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T11:25:51Z","timestamp":1726831551000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Spatio-Temporal Predictive Modeling Techniques for Different Domains: a Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0259-1628","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3751-7025","authenticated-orcid":false,"given":"Manish","family":"Bhanu","sequence":"additional","affiliation":[{"name":"Computer science and engineering, Indian Institute of Technology Patna, Patna, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2471-2833","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Mendes-Moreira","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Inform\u00e1tica, Faculdade de Engenharia,Universidade do Porto, Porto, Portugal and LIAAD-INESC Porto L.A., Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5994-9024","authenticated-orcid":false,"given":"Joydeep","family":"Chandra","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India"}]}],"member":"320","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"145","article-title":"Transfer learning: Survey and classification","author":"Agarwal Nidhi","year":"2021","unstructured":"Nidhi Agarwal, Akanksha Sondhi, Khyati Chopra, and Ghanapriya Singh. 2021. 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