Yanfei Kang is an Associate Professor at the School of Economics and Management of Beihang University and Head of the Department of Quantitative Economics and Business Statistics. She received her PhD degree from Monash University and previously worked as a postdoctoral researcher at Monash University and Baidu Inc.’s Big Data Group as a senior R&D developer. Her research is published in various academic journals such as European Journal of Operational Research, International Journal of Forecasting, International Journal of Production Research, Statistical Analysis and Data Mining, Machine Learning, Pattern Recognition, among others. As a newly appointed Board member of the International Institute of Forecasters (IIF), Yanfei will be serving as the Program Chair for ISF 2025.
How did you become a forecaster?
My journey towards becoming a forecaster began during my PhD at Monash University, where I focused on time series event detection, classification, and analysis. In 2014, my PhD supervisor, Professor Kate Smith-Miles (now at the University of Melbourne), offered me a postdoc position that involved feature space analysis for time series forecasting. During this postdoc, I had the privilege of working with Professor Rob J Hyndman, an experience that significantly shaped my academic career. This opportunity inspired me to delve into various aspects of forecasting, including time series features, forecast combinations, probabilistic forecasting, forecast reconciliation, and intermittent demand forecasting. These methodologies have been applied across diverse fields such as online retail and energy.
What areas of forecasting interest you?
My research endeavours are primarily focused on tackling large-scale time series forecasting challenges by developing innovative methods in forecast combinations, probabilistic forecasting, hierarchical forecasting and intermittent demand forecasting. I also have close industrial collaborations, particularly in online retail and energy forecasting. More details on my research areas can be found on my website: http://yanfei.site.
How has the International Journal of Forecasting influenced you?
The International Journal of Forecasting (IJF), the official publication of IIF, has played a significant role in shaping my research and career in the field of forecasting. It has provided me with access to the latest advancements and methodologies in forecasting. The diverse range of topics covered in IJF has inspired many of my own research projects. For instance, the journal’s articles on hierarchical forecasting have significantly influenced my work in these areas. Publishing my own research in IJF has been a milestone in my career. For example, my first IJF paper in 2017 has got 192 citations till May 20, 2024. In addition, reviewing articles for IJF has enhanced my critical thinking and provided insights into the peer review process.
How has the International Symposium of Forecasting influenced you?
The International Symposium on Forecasting (ISF), organized by the IIF, has been particularly influential in my career. My first ISF in 2017 in Cairns was transformative, exposing me to a vibrant community of forecasters and providing invaluable insights into the field. This event also facilitated my ongoing close collaborations with notable researchers including Fotios Petropoulos and Anastasios Panagiotelis. Additionally, I have also taken the initiative to organize invited sessions at several ISF events, including the ISF 2019 in Greece, the Virtual ISF 2020, and the Virtual ISF 2021. Despite being unable to attend the ISF 2022 and ISF 2023 due to pandemic travel restrictions, my students presented our work, ensuring our ongoing active involvement and friendship in the forecasting community. Currently, we are preparing for the ISF 2025 in Beijing, China, and look forward to meeting you in Beijing!
What do you do in your free time?
In my free time, I love traveling with my family (my husband Feng Li and our two lovely sons). We enjoy exploring various natural landscapes, such as old-growth forests, grasslands, deserts, and mountains.