What’s New✨
- The fuma paper is accepted in Journal of the Operational Research Society
- New Paper: Forecast with Forecasts: Diversity Matters
- We are presenting at ISF2020 Invited Session
- The Deja Vu paper is accepted in the Journal of Business Research
- New Paper: Distributed ARIMA Models for Ultra-long Time Series
Recent Teaching 👨🏻🏫
- Distributed Statistical Computing (graduate level)
- Statistical Computing (undergraduate level)
- Tools for Data Science (spring term)
- Bayesian Data Analysis (graduate level)
Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. Feng obtained his Ph.D. degree in Statistics from Stockholm University, Sweden, in 2013. His research interests include Bayesian computation, econometrics and forecasting, and distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.
Slides of his recent talks are available here.
He also served as the Associate Dean for the School of Statistics and Mathematics at Central University of Finance and Economics since 2016.
Contact
Email: feng.li@cufe.edu.cn
Tel.: +86-(0)10-6177-6189
Education🎓
- Ph.D., 2013, Statistics, Stockholm University, Sweden.
- Thesis: Bayesian Modeling of Conditional Densities
- Supervisor: Prof. Mattias Villani
- Thesis opponent: Prof. Sylvia Frühwirth-Schnatter
- M.S., 2008, Statistics, Dalarna University, Sweden.
- B.S., 2007, Statistics, Renmin University of China.
Research Interests👨🔬
Bayesian Statistics, Econometrics and Forecasting, Distributed Learning
Research Grants
- Development of the Methodologies of Objective Performance Criteria Based Single-Armed Trials for The Clinical Evaluation of Traditional Chinese Medicine. funded by National Natural Science Foundation of China, (2020-). Major Investigator.
- Efficient Bayesian Flexible Density Methods with High Dimensional Financial Data funded by National Natural Science Foundation of China, (2016-2019). Principal investigator.
- Bayesian Multivariate Density Estimation Methods for Complex Data funded by Ministry of Education, China (2014-2016). Principal investigator
Awards🌟
- The 2014 Cramér Prize, for the best Ph.D. thesis in Statistics and Mathematical Statistics, awarded by the Swedish Statistical Society, Mar 2014, Sweden.
- International Society for Bayesian Analysis junior travel award, Jun 2012.
- Travel grant from the Swedish Knut and Alice Wallenberg Foundation, Aug 2011, Sweden.
- Outstanding graduate student, honored by Beijing Municipal Education Commission, Jul 2007, China.
Select Publications🗞️
- Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulo (2020). Déjà vu: A data-centric forecasting approach through time series cross-similarity (in press), Journal of Business Research.
- Xixi Li, Yanfei Kang, Feng Li (2020). Forecasting with time series imaging, Expert Systems with Applications 160: 113680.
- 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.
- Feng Li, Yanfei Kang (2018). Improving forecasting performance using covariate-dependent copula models. International Journal of Forecasting, 34(3):456–476.
- Feng Li, Mattias Villani, Robert Kohn (2010). Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities. Journal of Statistical Planning and Inference,140(12):3638–3654
- >>> show all publications
Working Papers⏳
- Forecast with Forecasts: Diversity Matters. (with Yanfei Kang, Wei Cao, Fotios Petropoulos)
- Forecasting: theory and practice. (with Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M.Z., Barrow, D.K., Bergmeir, C., Bessa, R.J., Boylan, J.E., Browell, J., Carnevale, C., Castle, J.L., Cirillo, P., Clements, M.P., Cordeiro, C., Cyrino Oliveira, F.L., De Baets, S., Dokumentov, A., Fiszeder, P., Franses, P.H., Gilliland, M., Gönül, M.S., Goodwin, P., Grossi, L., Grushka-Cockayne, Y., Guidolin, M., Guidolin, M., Gunter, U., Guo, X., Guseo, R., Harvey, N., Hendry, D.F., Hollyman, R., Januschowski, T., Jeon, J., Jose, V.R.R., Kang, Y., Koehler, A.B., Kolassa, S., Kourentzes, N., Leva, S., Litsiou, K., Makridakis, S., Martinez, A.B., Meeran, S., Modis, T., Nikolopoulos, K., Önkal, D., Paccagnini, A., Panapakidis, I., Pavía, J.M., Pedio, M., Pedregal Tercero, D.J., Pinson, P., Ramos, P., Rapach, D., Reade, J.J., Rostami-Tabar, B., Rubaszek, M., Sermpinis, G., Shang, H.L., Spiliotis, E., Syntetos, A.A., Talagala, P.D., Talagala, T.S., Tashman, L., Thomakos, D., Thorarinsdottir, T., Todini, E., Trapero Arenas, J.R., Wang, X., Winkler, R.L., Yusupova, A., Ziel, Z.)
- Distributed Forecasting for Ultra-long Time Series. (with Xiaoqian Wang, Yanfei Kang and Rob J Hyndman)
- Least squares approximation for a distributed system. (with Xuening Zhu and Hansheng Wang)
- FFORMPP: Feature-based forecast model performance prediction. (with Thiyanga S. Talagala and Yanfei Kang)