KLLAB.org

Collaborators | Visitors

We invite collaborators to visit our KLLAB every year, and we also organize focused workshops with them on a specific theme. Our research network reaches Australia, UK, Sweden, the US, and other countries.

Academic Visitors

  • 2024 Dec, Minh-Ngoc Tran, Associate Professor at the Business Analytics discipline, the University of Sydney Business School
  • 2024 Jul, Mattias Villani, Professor of Statistics, Stockholm University, Swenden
  • 2024 Jul, Matias Quiroz, Senior Lecturer at the School of Mathematical and Physical Sciences at the University of Technology Sydney
  • 2023, Han Li, Associate Professor at the Centre for Actuarial Studies, Department of Economics, The University of Melbourne.
    • Han Wang, Wen Wang, Feng Li, Yanfei Kang and Han Li (2024). “Catastrophe Duration and Loss Prediction via Natural Language Processing”. Variance, Forthcoming
  • 2023, Anastasios Panagiotelis, Associate Professor of Business Analytics at the University of Sydney Business School. He is also a Director of the International Institute of Forecasters, Deputy Head of Business Analytics at the University of Sydney Business School (Past visits were in 2016, 2018, 2019)
    • Bohan Zhang, Anastasios Panagiotelis and Yanfei Kang (2024). “Discrete Forecast Reconciliation”. European Journal of Operational Research
    • Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis and Feng Li (2023). “Optimal Reconciliation with Immutable Forecasts”. European Journal of Operational Research, Vol. 308(1), pp. 650-660.
  • 2019 Jul, Kate Smith-Miles, Professor of Applied Mathematics in the School of Mathematics and Statistics at The University of Melbourne,  Laureate Fellowship from the Australian Research Council.
  • 2019 April, Fotios Petropoulos, Associate Professor at the University of Bath, UK, and Editor of the International Journal of Forecasting.
    • Li Li, Yanfei Kang, Fotios Petropoulos and Feng Li (2023). “Feature-Based Intermittent Demand Forecast Combinations: Accuracy and Inventory Implications”. International Journal of Production Research, Vol. 61(22), pp. 7557-7572.
    • Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos and Feng Li (2022). “The Uncertainty Estimation of Feature-Based Forecast Combinations”. Journal of the Operational Research Society, Vol. 73(5), pp. 979-993.
    • Fotios Petropoulos, et al (2022). “Forecasting: Theory and Practice”. International Journal of Forecasting, Vol. 38(3), pp. 705-871.
    • Yanfei Kang, Wei Cao, Fotios Petropoulos and Feng Li (2022). “Forecast with Forecasts: Diversity Matters”. European Journal of Operational Research, Vol. 301(1), pp. 180-190.
    • Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li and Vassilios Assimakopoulos (2021). “Déjà vu: A Data-Centric Forecasting Approach through Time Series Cross-Similarity”. Journal of Business Research, Vol. 132pp. 719-731.
    • Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos and Feng Li (2022). “The Uncertainty Estimation of Feature-Based Forecast Combinations”. Journal of the Operational Research Society, Vol. 73(5), pp. 979-993.
  • 2019 Sune Karlsson, Professor of Statistics, School of Business, Örebro University, Sweden.
  • 2019 Feb-Mar, Thiyanga S. Talagala, Senior Lecturer in the Department of Statistics, Faculty of Applied Sciences at the University of Sri Jayewardenepura.
    • Thiyanga S. Talagala, Feng Li and Yanfei Kang (2022). “FFORMPP: Feature-Based Forecast Model Performance Prediction”. International Journal of Forecasting, Vol. 38(3), pp. 920-943.
  • 2018 Dietrich von Rosen, Department of Energy and Technology, Swedish University of Agricultural Sciences. Editor-in-Chief for Journal of Multivariate Analysis.
    • Chengcheng Hao, Feng Li and Dietrich von Rosen (2020). “A Bilinear Reduced Rank Model”. In Contemporary Experimental Design, Multivariate Analysis and Data Mining. Springer Nature
  • 2018 Jun, Christoph Bergmeir, Senior Research Fellow in Data Science in the Monash Faculty of Information Technology, Monash University.
    • Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang and Christoph Bergmeir (2021). “Improving the Accuracy of Global Forecasting Models Using Time Series Data Augmentation”. Pattern Recognition, Vol. 120pp. 108148.
  • 2017 Nov-Dec, Rob J Hyndman FAA FASSA, Professor of Statistics, Department of Econometrics & Business Statistics at Monash University, Editor-in-Chief of the International Journal of Forecasting (2005-2018).
    • Xiaoqian Wang, Yanfei Kang, Rob J. Hyndman and Feng Li (2023). “Distributed ARIMA Models for Ultra-Long Time Series”. International Journal of Forecasting, Vol. 39(3), pp. 1163-1184.
    • Xiaoqian Wang, Rob J. Hyndman, Feng Li and Yanfei Kang (2023). “Forecast Combinations: An over 50-Year Review”. International Journal of Forecasting, Vol. 39(4), pp. 1518-1547.
    • tsfeatures: Time Series Feature Extraction
    • Yanfei Kang, Rob J Hyndman,  & Feng Li* (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics, Statistical Analysis and Data Mining 13(4): 354-376.
  • 2017 Måns Magnusson, Assistant proffessor in Statistics at Department of Statistics, Uppsala University

Collaborators (not visited us yet)

Our collaborators (who have not visited us yet) are available from Prof Yanfei Kang and Prof Feng Li‘s Google Scholar profiles.