Spatio-Temporal Predictive Modeling Techniques for Different Domains: a Survey

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Kumar, R (Autor) Outra; Bhanu, M (Autor) Outra; João Mendes-Moreira (Autor) FEUP; Chandra, J (Autor) Outra

Revista

Título: ACM Computing Surveys

ISSN: 0360-0300

Editora: ACM

Outras Informações

ID Authenticus: P-016-Z8D

DOI: 10.1145/3696661

Abstract (EN): <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 paper 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>

Idioma: Inglês

Tipo (Avaliação Docente): Científica