Feng Li
Personal Details
First Name: | Feng |
Middle Name: | |
Last Name: | Li |
Suffix: | |
RePEc Short-ID: | pli521 |
| |
https://feng.li/ | |
Guanghua School of Management Peking University, Beijing 100871, China | |
+861062747602 | |
Twitter: | f3ngli |
Mastodon: | |
Terminal Degree: | 2013 (from RePEc Genealogy) |
Affiliation
Department of Business Statistics and Econometrics
Guanghua School of Management
Peking University
Beijing, Chinahttp://www.gsm.pku.edu.cn/statistic/index.html
RePEc:edi:dbpkucn (more details at EDIRC)
Research output
Jump to: Working papers Articles ChaptersWorking papers
- Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022.
"Optimal reconciliation with immutable forecasts,"
Papers
2204.09231, arXiv.org.
- Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
- Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications," Papers 2204.08283, arXiv.org, revised Aug 2022.
- Li Li & Yanfei Kang & Feng Li, 2021.
"Bayesian forecast combination using time-varying features,"
Papers
2108.02082, arXiv.org, revised Jun 2022.
- Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
- Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020.
"Forecasting: theory and practice,"
Papers
2012.03854, arXiv.org, revised Jan 2022.
- Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
- Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020.
"Distributed ARIMA Models for Ultra-long Time Series,"
Monash Econometrics and Business Statistics Working Papers
29/20, Monash University, Department of Econometrics and Business Statistics.
- Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
- Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
- Yanfei Kang & Rob J Hyndman & Feng Li, 2018. "Efficient generation of time series with diverse and controllable characteristics," Monash Econometrics and Business Statistics Working Papers 15/18, Monash University, Department of Econometrics and Business Statistics.
- Li, Feng & Villani, Mattias & Kohn, Robert, 2010. "Modeling Conditional Densities Using Finite Smooth Mixtures," Working Paper Series 245, Sveriges Riksbank (Central Bank of Sweden).
- Li, Feng & Villani, Mattias & Kohn, Robert, 2009. "Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities," Working Paper Series 233, Sveriges Riksbank (Central Bank of Sweden).
Articles
- Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023.
"Optimal reconciliation with immutable forecasts,"
European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
- Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022. "Optimal reconciliation with immutable forecasts," Papers 2204.09231, arXiv.org.
- Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023.
"Distributed ARIMA models for ultra-long time series,"
International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
- Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.
- Li, Li & Kang, Yanfei & Li, Feng, 2023.
"Bayesian forecast combination using time-varying features,"
International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
- Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.
- Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2023. "Feature-based intermittent demand forecast combinations: accuracy and inventory implications," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7557-7572, November.
- Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
- Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.
- Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
- Xiaoqian Wang & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "The uncertainty estimation of feature-based forecast combinations," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(5), pages 979-993, May.
- Anderer, Matthias & Li, Feng, 2022. "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1405-1414.
- Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022.
"Forecasting: theory and practice,"
International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
- Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
- Rui Pan & Tunan Ren & Baishan Guo & Feng Li & Guodong Li & Hansheng Wang, 2022. "A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1691-1700, October.
- Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
- 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, Public Library of Science, vol. 14(11), pages 1-18, November.
- Feng Li & Zhuojing He, 2019. "Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1632528-163, January.
- Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.
- Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
Chapters
- Li Li & Feng Li & Yanfei Kang, 2023. "Forecasting Large Collections of Time Series: Feature-Based Methods," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 251-276, Palgrave Macmillan.
More information
Research fields, statistics, top rankings, if available.Statistics
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Co-authorship network on CollEc
NEP Fields
NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 9 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.- NEP-FOR: Forecasting (7) 2018-10-15 2019-10-28 2020-09-07 2021-08-16 2022-03-21 2022-05-09 2022-05-16. Author is listed
- NEP-ECM: Econometrics (6) 2009-12-11 2010-10-30 2018-10-15 2019-10-28 2020-09-07 2021-08-16. Author is listed
- NEP-ETS: Econometric Time Series (4) 2018-10-15 2019-10-28 2020-09-07 2021-08-16
- NEP-BAN: Banking (1) 2022-03-21
- NEP-CWA: Central and Western Asia (1) 2022-03-21
- NEP-ISF: Islamic Finance (1) 2021-08-16
- NEP-ORE: Operations Research (1) 2019-10-28
- NEP-UPT: Utility Models and Prospect Theory (1) 2022-03-21
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