- New Paper: Forecasting reconciliation with a top-down alignment of independent level forecasts
- 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
- The forecasting with time series imaging paper is accepted in Expert Systems with Applications
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.
He also served as the Associate Dean for the School of Statistics and Mathematics at Central University of Finance and Economics since 2016.
- Ph.D., 2013, Statistics, Stockholm University, Sweden.
- M.S., 2008, Statistics, Dalarna University, Sweden.
- B.S., 2007, Statistics, Renmin University of China.
Bayesian Statistics, Econometrics and Forecasting, Distributed Learning
- 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
- 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.
- Forecasting reconciliation with a top-down alignment of independent level forecasts. (with Matthias Anderer)
- 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)
- FFORMPP: Feature-based forecast model performance prediction. (with Thiyanga S. Talagala and Yanfei Kang)
- Xuening Zhu, Feng Li*, & Hansheng Wang (2021). Least squares approximation for a distributed system. Journal of Computational and Graphical Statistics. (in press).
- Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, & Feng Li* (2021). The uncertainty estimation of feature-based forecast combinations, Journal of the Operational Research Society. (in press).
- Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li*, & Vassilios Assimakopoulo (2021). Déjà vu: A data-centric forecasting approach through time series cross-similarity, Journal of Business Research. (in press).
- 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.
- Chengcheng Hao, Feng Li, & Dietrich von Rosen (2020). A Bilinear Reduced Rank Model, In Jianqing Fan and Jianxin Pan (eds.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer.
- Hyndman, R.J., & Athanasopoulos, G.著. 预测：方法与实践（第2版），康雁飞、李丰（译）https://otexts.com/fppcn/
- Hannah M Bailey, Yi Zuo, Feng Li, Jae Min, Krishna Vaddiparti, Mattia Prosperi, Jeffrey Fagan, Sandro Galea, & Bindu Kalesan (2019). Changes in patterns of mortality rates and years of life lost due to firearms in the united states,1999 to 2016: A joinpoint analysis. PLoS One, 14(11).
- Feng Li & Zhuojing He (2019). Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? Cogent Economics & Finance, page 1632528.
- Feng Li & Yanfei Kang (2018). Improving forecasting performance using covariate-dependent copula models. International Journal of Forecasting, 34(3):456–476.
- Elizabeth C Pino, Yi Zuo, Camila Maciel DeOlivera, Shruthi Mahalingaiah, Olivia Keiser, Lynn L Moore, Feng Li, Ramachandran S Vasan, Barbara E Corkey & Bindu Kalesan (2018). Cohort profile: The multistudy diabetes research (multitude) consortium. BMJ Open, 8(5):e020640.
- 李丰（2016）大数据分布式计算与案例。中国人民大学出版社。ISBN 9787300230276.
- [ Online version ]
- Feng Li (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1.
- Feng Li & Mattias Villani (2013). Efficient Bayesian multivariate surface regression. Scandinavian Journal of Statistics, 40(4):706–723.
- Feng Li, Mattias Villani & Robert Kohn (2011.). Modeling conditional densities using finite smooth mixtures. In Kerrie Mengersen, Christian Robert, Mike Titterington (eds.), Mixtures: estimation and applications, pages 123–144. John Wiley & Sons Inc, Chichester.
- 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