Prof. Dr. Shaolong Sun Appointed Section Editor-in-Chief of Section “AI Forecasting” in Forecasting
We are pleased to announce the appointment of Prof. Dr. Shaolong Sun as the Section Editor-in-Chief of the new “AI Forecasting” Section in the journal Forecasting (ISSN: 2571-9394).
Prof. Dr. Sun earned his PhD in management science and engineering from the University of Chinese Academy of Sciences in 2019 and is currently a professor at Xi’an Jiaotong University, China. His research centers on data intelligence and management, economic analysis and forecasting—particularly AI-driven forecasting techniques—and their applications to digital culture, tourism, and smart management. Prof. Dr. Sun has an outstanding track record in his field. He has published over 100 research papers, including numerous articles in top-tier international journals such as Tourism Management and the Journal of Travel Research. He also serves in multiple editorial roles, for example as Associate Editor or Editorial Board Member for several journals, including Data Science and Management, Journal of Systems Science & Complexity, and Financial Innovation, among others. In recognition of his scholarly impact, Prof. Dr. Sun has received prestigious honors—he was awarded the “Top 100 Outstanding Doctoral Dissertation Award” from Chinese Academy of Sciences, in addition to a national award for outstanding doctoral work in systems science and engineering. He has also been named among the world’s Top 2% most-cited scientists, according to a Stanford University study.
Beyond academia, his work has significantly influenced policy and governance. He has authored 30 policy research reports, 10 of which received important directives from China’s national leaders, with several of his recommendations adopted by key government agencies such as the Ministry of Commerce, the People’s Bank of China, and the State Administration of Foreign Exchange. These contributions demonstrate how his data-driven forecasting research has supported high-level national decision-making and development priorities. Prof. Sun’s blend of cutting-edge research, real-world impact, and international engagement makes him exceptionally well-suited to lead the AI Forecasting Section and foster its growth as a hub for innovation in predictive science.
The following is a short Q&A with Prof. Dr. Shaolong Sun, in which he shares his vision for the “AI Forecasting” Section, insights into methodological innovations in AI-driven forecasting, and perspectives on the field’s future.
1. Could you briefly introduce your main research areas and achievements?
My research focuses on intelligent forecasting, with an emphasis on developing advanced data-driven models that integrate multisource and multimodal data—including text, time series, and knowledge graphs—to enhance prediction accuracy and interpretability. The core objective of my work is to advance forecasting methods that are not only theoretically rigorous but also practically relevant to complex social and economic systems. My primary application areas include tourism demand forecasting, financial market prediction, energy systems modeling, and AI-driven service innovation. Over the years, I have led several major research projects, including a National Key R&D Program for Young Scientists that explores evaluation mechanisms for the modern service industry, and a National Science Foundation (NSFC) project focused on data-driven tourism forecasting. I have published more than fifty papers as first or corresponding author in high-impact journals, proposing innovative frameworks such as knowledge graph-based recommendation systems and multimodal forecasting architectures. Many of my research outcomes have directly informed policy decisions—for instance, eight of my advisory reports received directives from national leaders and seventeen were adopted by central government agencies—demonstrating the societal value of scientific forecasting and its integration into evidence-based governance. These experiences illustrate how advanced forecasting research can contribute to data-driven governance and deliver tangible benefits to society.
2. What appealed to you about Forecasting that made you want to take on the role of Section Editor-in-Chief of “AI Forecasting”?
I was deeply attracted to Forecasting because of its clear vision and strong commitment to advancing predictive science at the intersection of data analytics, artificial intelligence, and applied modeling. The journal’s focuses on methodological rigor and interdisciplinary collaboration aligns closely with my own academic philosophy. Moreover, the launch of the “AI Forecasting” Section is a timely initiative that responds to the growing need for innovative forecasting approaches in an era of data explosion and digital transformation. In my view, this new section addresses a critical gap by highlighting how cutting-edge AI techniques can be applied to improve forecasting across different domains.
I also believe that this section will serve as a high-level platform for presenting frontier research on machine learning, deep learning, and AI-powered predictive models across diverse application areas. The open access model of MDPI ensures that knowledge is disseminated efficiently and globally, promoting visibility and accessibility for researchers worldwide. This inclusive approach was very appealing to me. My goal as Section Editor-in-Chief is to strengthen the scientific influence of Forecasting by encouraging cross-disciplinary dialogue, supporting innovative Special Issues, and maintaining a rigorous yet constructive peer-review process that ensures both quality and impact. In short, the combination of a forward-looking scope, global reach, and commitment to excellence made this opportunity irresistible.
3. What are your expectations and suggestions for the future development of the “AI Forecasting” Section and the journal?
My vision is to establish the “AI Forecasting” Section as a leading international platform for cutting-edge research in artificial intelligence and predictive analytics. The Section should promote academic excellence by attracting contributions that bridge methodological innovation with real-world application—for example, from explainable AI, causal modeling, and hybrid approaches to probabilistic prediction, real-time forecasting, and intelligent decision-making. I believe that the future of forecasting research lies in the integration of diverse data sources and the fusion of domain knowledge with AI-driven models, which will enable more adaptive and interpretable predictions. In particular, combining traditional statistical techniques with modern AI (such as foundation models like large language models) offers exciting possibilities for improving both the accuracy and explainability of forecasts in complex scenarios.
To further enhance the journal’s influence, I intend to encourage collaboration between academia and industry and to organize high-quality Special Issues on emerging topics—such as large-scale multimodal forecasting, the role of foundation models in prediction, and AI applications in areas like digital tourism and smart cities. We will also actively expand international editorial participation, essentially building a truly global community of forecasting scholars. Encouraging diverse global contributors will spark new ideas and ensure that our Section covers developments from around the world. Additionally, I plan to uphold strong standards of reproducibility and transparency in the research we publish. By promoting open science practices—for instance, encouraging authors to share data and code—we can ensure that the findings in AI forecasting are robust, trusted, and readily usable by both researchers and practitioners. In summary, my aim is to foster an innovative, collaborative, and international forum that not only pushes the frontier of forecasting science but also provides reliable knowledge to inform policy and practice.
4. Do you have any suggestions for young researchers in this field?
Young researchers in forecasting and data-driven modeling should strive to cultivate both methodological depth and an application-oriented vision. A solid foundation in statistics, machine learning, and computational modeling is essential, but it is equally important to understand the specific contexts in which forecasting models are applied. Real progress often comes from the ability to connect technical innovation with meaningful societal or industrial problems. I encourage early-career scholars to engage in interdisciplinary collaboration, communicate their ideas clearly, and remain open to new perspectives. Engaging with the international research community—by attending conferences, exchanging ideas with peers abroad, and forming cross-border collaborations—can also spark new insights and broaden one’s horizons.
The process of scientific research is often long and challenging, so persistence, curiosity, and critical thinking are some of the most valuable assets a researcher can have. Breakthroughs in forecasting (as in any field) often arise from unconventional thinking and continuous experimentation, so do not be afraid to explore creative approaches. At the same time, maintaining academic integrity and pursuing research with long-term impact are crucial. If you focus on problems that really matter and contribute to the broader community, your work will ultimately leave a lasting mark on both science and society.
We congratulate Prof. Shaolong Sun on his new role as Section Editor-in-Chief and wish him every success. We look forward to his leadership in advancing the “AI Forecasting” Section and his contributions to the global forecasting research community in the years to come.