Malaysian Journal of Computer Science is a prominent academic journal dedicated to advancing the field of computer science, published by the esteemed University of Malaya, Faculty of Computer Science & Information Technology. With historical coverage spanning from 1996 to 2024, this journal plays a vital role in disseminating innovative research and developments in various areas of computer science. Recognized with a 2023 Scopus rank in the top half of its category, ranked #137 out of 232, it holds a Q3 quartile position in the field of General Computer Science. The journal serves as a platform for researchers, professionals, and students to share groundbreaking findings and foster collaboration within the community. While currently not offering open access, its commitment to quality and academic integrity remains unwavering, making it an essential source for anyone interested in the latest advancements in informatics and technology.

Forecasting is an esteemed open-access journal published by MDPI, dedicated to advancing the fields of Computational Theory and Mathematics, Computer Science Applications, Decision Sciences, and Economics, since its inception in 2019. With its base in Switzerland, the journal aims to provide a fundamental platform for researchers, professionals, and students aiming to share high-quality research on mathematical methods for forecasting and prediction model development. As of 2023, Forecasting holds an impressive Q1 ranking in Economics, Econometrics, and Finance, alongside Q2 rankings in Computational Theory and Mathematics, Computer Science Applications, and Decision Sciences, reflecting its rigorous peer-review process and scientific impact. Forecasting has carved a niche as a leading publication, inviting high-quality contributions that stimulate innovation in forecasting methodologies across various applications in decision-making and economic analysis. This impact is further underscored by its commitment to open access, ensuring that research is readily accessible to the global academic community.

STATISTICA NEERLANDICA is a prestigious peer-reviewed journal published by Wiley, focusing on the fields of statistics and probability. Established in 1946 and addressing key issues in statistical theory and its applications, the journal has significantly contributed to the development of modern statistical practices. With an impressive Q2 categorization in both Statistics and Probability, as well as Statistics, Probability, and Uncertainty, STATISTICA NEERLANDICA stands out within its field, ranking in the 62nd percentile among its peers in mathematics, specifically in statistics and probability. Researchers, professionals, and students can benefit from its rigorous scholarship and innovative methodologies, aiding in the advancement of statistical science. Although the journal does not operate under an open access model, it maintains a commitment to disseminating high-quality research, making it a vital resource for those engaged in statistical inquiry.

Applied Stochastic Models in Business and Industry, published by Wiley, is a preeminent journal dedicated to advancing the field of stochastic modeling in various business and industry contexts. With an ISSN of 1524-1904 and an E-ISSN of 1526-4025, this journal serves as a vital platform for researchers and practitioners aiming to leverage stochastic techniques to address complex decision-making and operational challenges. The journal holds prestigious rankings, including Q2 in Business, Management and Accounting, and has converged years spanning from 1999 to 2024. It is distinguished by its wide scope, focusing on all aspects of modeling and simulation, as well as decision sciences and operations research. Although it does not operate as an open-access journal, its significant impact factor and Scopus rankings ensure that the valuable insights published within reach a broad audience. By fostering high-quality research and discourse, Applied Stochastic Models in Business and Industry plays a crucial role in driving innovation and excellence in its fields.

International Journal of Forecasting, published by Elsevier, stands as a premier platform in the domain of forecasting, catering to a diverse audience of researchers, professionals, and students. With its strong commitment to advancing theoretical and practical knowledge, this journal covers a broad spectrum of topics related to forecasting methods, applications, and empirical research across various fields, including economics, business, and social sciences. Holding an impressive impact factor and recognized as a Q1 journal in Business and International Management, it ranks #14 out of 443 in its category, placing it in the 96th percentile on Scopus. The journal has been actively published since 1985 and continues to provide meaningful insights that drive informed decision-making. Despite not being open access, the rigorous peer-review process ensures that the highest quality research is showcased, making it an essential read for those looking to stay at the forefront of forecasting methodologies and applications.

Statistical Analysis and Data Mining is a leading journal published by WILEY, dedicated to exploring the latest advancements in statistical methods and data mining techniques. With an ISSN of 1932-1864 and an E-ISSN of 1932-1872, this journal serves as a significant platform for researchers and professionals in statistical analysis, computer science applications, and information systems. Covering a wide range of topics from innovative analytical methodologies to emerging data mining algorithms, the journal aims to disseminate high-quality research that contributes to the evolving landscape of data science. Ranked in the Q2 category for the fields of Analysis, Computer Science Applications, and Information Systems in 2023, it emphasizes its relevance and impact within academia. While it offers limited Open Access options, the insights shared in this publication are integral for those wishing to stay ahead in fast-paced research and data-driven industries. Since its inception in 2008 and continuing through 2024, Statistical Analysis and Data Mining invites submissions that reflect rigorous empirical research coupled with practical implications, making it a vital resource for the academic community.

Data Science in Finance and Economics is a pioneering journal published by the American Institute of Mathematical Sciences (AIMS), devoted to the intersection of data science with the fields of finance and economics. Established as an open-access journal since 2021, it aims to disseminate high-quality research that unravels complex financial phenomena and economic models through innovative data-driven methodologies. With a commitment to advancing knowledge in this rapidly evolving discipline, the journal encourages submissions that encompass theoretical studies, empirical research, and application-based articles from both academia and industry. While the journal is relatively new, its potential to significantly influence the discourse in finance and economics is profound, offering researchers, professionals, and students an invaluable resource to stay abreast of current trends and methodologies in data science. For access to cutting-edge research and insights, visit AIMS and contribute to the ongoing conversation in this essential field.

REVSTAT-Statistical Journal, published by the Instituto Nacional de Estatística (INE)Open Access model established in 2003, REVSTAT promotes the free dissemination of high-quality research, ensuring broad accessibility to its published works. Although it currently holds a Q4 category ranking in Statistics and Probability according to Scopus, the journal aims to enhance its contributions to the statistical community by featuring innovative methodologies, theoretical advancements, and applied statistical research. With a convergence period extending from 2010 to 2024, REVSTAT invites submissions that not only enrich the discipline but also encourage interdisciplinary collaboration. Its commitment to developing statistical knowledge makes it a noteworthy avenue for anyone seeking to engage with contemporary statistical discourse.

Sigmae is an emerging academic journal published by Universidade Federal de Alfenas, focusing on interdisciplinary research in the fields of social sciences, humanities, and natural sciences. With its ISSN 2317-0840, Sigmae aims to provide a platform for innovative ideas and rigorous research, fostering collaboration among scholars and practitioners. Although the journal is currently operating without an Open Access model, it ensures high-quality peer-reviewed articles that contribute to advancements in various domains of knowledge. The journal is committed to enhancing visibility for Brazilian research on both national and international stages. As a vital resource for researchers, professionals, and students, Sigmae invites submissions that explore theoretical, methodological, and empirical dimensions within its broad scope. With a goal to expand its academic contributions and enhance its standing in the research community, Sigmae is poised to become a key player in shaping contemporary discourse.

JOURNAL OF TIME SERIES ANALYSIS, published by Wiley, is a premier academic journal dedicated to the field of time series analysis, serving as a pivotal resource for researchers, professionals, and students in applied mathematics and statistics. With a strong commitment to advancing scholarly knowledge since its inception in 1980, the journal focuses on innovative methodologies and applications in time series data, highlighting its relevance in various domains, including economics, finance, and environmental studies. Although the journal currently does not offer open access, it maintains a robust reputation, reflected in its 2023 Q1 and Q2 quartile rankings in applied mathematics and statistics, respectively. The contributions published in this journal are instrumental for advancing theoretical frameworks and practical applications in data analysis. Stay informed of cutting-edge research and methodologies by engaging with the JOURNAL OF TIME SERIES ANALYSIS, which is accessible through various academic databases.