Forecasting

Forecasting is a peer-reviewed, open access journal (ISSN 2571-9394) published by MDPI, dedicated to advancing the theory, methodology, and application of forecasting across diverse scientific and professional domains. The journal’s scope encompasses the development and evaluation of forecasting models, time series analysis, machine learning for prediction, and uncertainty quantification, with particular emphasis on interdisciplinary approaches that bridge statistics, economics, engineering, environmental science, and operations research. It publishes a range of article types, including original research articles, comprehensive review papers, methodological advances, and case studies that demonstrate practical forecasting solutions. The target audience comprises academic researchers, data scientists, statisticians, policy analysts, and industry professionals who rely on accurate predictions for decision-making in fields such as finance, supply chain management, energy, climate science, and public health. As a Q1 journal in its category according to JCR Quartile rankings, Forecasting holds a strong standing within the field, reflecting its high citation impact and rigorous editorial standards. Notably, the journal operates under an open access model, ensuring that all published content is freely accessible to a global readership, which enhances the dissemination and reproducibility of forecasting research. With a commitment to rapid publication and a broad thematic reach, Forecasting serves as a premier platform for innovative work that shapes both theoretical understanding and real-world forecasting practice.