康雁飞

北京航空航天大学经济管理学院
副教授、博士生导师
数量经济与商务统计系主任      
北航“卓越百人计划(2016)”和“青年拔尖人才支持计划(2021)”入选者

邮件：yanfeikang@buaa.edu.cn 
主页：http://yanfei.site 
地址：北京市海淀区学院路37号新主楼A931 
欢迎对我的研究领域感兴趣的同学与我邮件联系或者访问我的实验室：KLLAB.org！

康雁飞，现任北京航空航天大学经济管理学院副教授、博士生导师、数量经济与商务统计系主任。2014年博士毕业于莫纳什大学，师从澳大利亚科学院院士 Kate Smith-Miles 教授和 Danijel Belusic 教授，2014-2015年于莫纳什大学从事博士后研究，合作导师为两位澳大利亚科学院院士 Kate Smith-Miles 教授和 Rob Hyndman 教授，2015-2016年就职于百度大数据部。研究方向为时间序列预测等。共承担科研项目10余项，其中主持国家自然科学基金2项，参与国家重点研发计划课题、阿里巴巴创新研究计划各1项。在 European Journal of Operational Research, International Journal of Forecasting 等国际权威期刊发表论文30篇，同时译著《预测：方法与实践》，著有 《大数据分布式存储与计算》和《统计计算》。曾在国际预测大会、ICDM、IJCNN、IEEE CIDM、世界贝叶斯大会等受邀做报告。担任International Journal of Forecasting (SSCI, JCR Q1)和R Journal (SCI, JCR Q1) 副主编、国际预测者学会理事（Director of  the International Institute of Forecasters)、中国统计教育学会理事、北京大数据协会理事等。先后入选北航“卓越百人计划”和北航“青年拔尖人才计划”。

教育背景

  • 2010.09 – 2014.08 博士 澳大利亚莫纳什大学
  • 2009.09 – 2010.07 统计学研究生 中国人民大学
  • 2005.09 – 2009.07 统计学本科 山东财经大学

研究兴趣

大规模时间序列预测；预测驱动的管理决策；大数据分析

工作经历

  • 2016.11 – 今 北京航空航天大学经济管理学院 副教授、硕士生导师、博士生导师
  • 2015.08 – 2016.08 百度大数据部 大数据高级研发工程师
  • 2014.08 – 2015.07 澳大利亚莫纳什大学 博士后研究员

科研项目

  • 2022年 – 2025年，国家自然科学基金面上项目：大规模时间序列的联合预测研究：全局模型视角，负责人
  • 2021年 – 2024年，北京航空航天大学“青年拔尖人才支持计划”，负责人
  • 2021年 – 2022年，阿里巴巴创新研究计划：电商场景下的复杂时间序列预测问题研究，主要参与者
  • 2018年 – 2020年，国家自然科学基金青年项目：基于实例空间的时间序列预测研究，负责人
  • 2017年 – 2019年，北京航空航天大学“卓越百人计划”，负责人
  • 2017年 – 2018年，北京航空航天大学基本科研业务项目：大数据理论及应用研究，主要参与者
  • 2014年 – 2015年，Stress-testing algorithms: generating new test instances to elicit insights, funded by Australian Research Council，主要参与者（博士后主要工作）

在审论文

  1. Christoph Bergmeir, Frits de Nijs, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Evgenii Genov, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Pérez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan (2022). Comparison and Evaluation of Methods for a Predict+ Optimize Problem in Renewable Energy. Working paper.
  2. Xiaoqian Wang, Yanfei Kang, Feng Li (2022). Another look at forecast trimming for combinations: robustness, accuracy and diversity. Working paper.

学术论文

  1. Bohan Zhang, Anastasios Panagiotelis, Yanfei Kang* (2023). Discrete forecast reconciliation. European Journal of Operational Research 318(1): 143-153. Online. Working paper.
  2. Shengjie Wang, Yanfei Kang, Fotios Petropoulos (2024). Combining Probabilistic Forecasts of Intermittent Demand. European Journal of Operational Research 315(3): 1038–1048. Online. Working paper.
  3. Yun Bai, Ganglin Tian, Yanfei Kang*, Suling Jia (2023). A hybrid ensemble method with negative correlation learning for regression. Machine Learning 112: 3881–3916. Online. Working paper.
  4. Spyros Makridakis, Fotios Petropoulos, Yanfei Kang* (2023). Large Language Models: Their success and impact. Forecasting 5(3), 536-549, doi: 10.3390/forecast5030030. Online.
  5. Spyros Makridakis, Fotios Petropoulos, Yanfei Kang* (2023). The Impact of Large Language Models like ChatGPT on Forecasting. Foresight: The International Journal of Applied Forecasting 69:61-62. Online.
  6. Li Li, Feng Li and Yanfei Kang* (2023), “Forecasting Large Collections of Time Series: Feature-Based Methods”, In Forecasting with Artificial Intelligence: Theory and Applications. Cham , pp. 251-276. Springer Nature Switzerland. Online.
  7. Xiaoqian Wang, Rob Hyndman, Feng Li, Yanfei Kang* (2023). Forecast combinations: an over 50-year review. International Journal of Forecasting 39(4): 1518-1547, doi: 10.1016/j.ijforecast.2022.11.005. Online. Working paper.
  8. Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis, Feng Li (2022). Optimal reconciliation with immutable forecasts. European Journal of Operational Research 308(2): 650-660, doi: 10.1016/j.ejor.2022.11.035. Online. Working paper.
  9. Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li (2022). Feature-based intermittent demand forecast combinations: accuracy and inventory implications. International Journal of Production Research 61(22): 7557-7572, doi: 10.1080/00207543.2022.2153941. Online. Working paper.
  10. Li Li, Yanfei Kang, Feng Li (2023). Bayesian forecast combination using time-varying features. International Journal of Forecasting 39(3): 1187-1302, doi: 10.1016/j.ijforecast.2022.06.002. Online. Working paper.
  11. Xiaoqian Wang, Yanfei Kang, Rob Hyndman, Feng Li (2023). Distributed ARIMA models for ultra-long time series. International Journal of Forecasting 39(3): 1163-1184, doi: 10.1016/j.ijforecast.2022.05.001. Online. Working paper. Spark implementation.
  12. Xixi Li, Fotios Petropoulos, Yanfei Kang* (2023). Improving forecasting by subsampling seasonal time series. International Journal of Production Research 61(3): 976-992, doi: 10.1080/00207543.2021.2022800. Online. Working paper.
  13. 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., Li, F., 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. (2022). Forecasting: theory and practice. International Journal of Forecasting 38(3): 705-871, doi: 10.1016/j.ijforecast.2021.11.001. Online. Working paper. Bookdown version.
  14. Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li (2021). Forecast with forecasts: Diversity matters. European Journal of Operational Research 301(1): 180-190, doi: 10.1016/j.ejor.2021.10.024. Online. Working paper.
  15. Xixi Li#, Yun Bai#, Yanfei Kang* (2022). Exploring the social influence of Kaggle virtual community on the M5 competition. International Journal of Forecasting 38(4): 1507-1518, doi: 10.1016/j.ijforecast.2021.10.001. Online. Working paper.
  16. Evangelos Theodorou#, Shengjie Wang#, Yanfei Kang*, Evangelos Spiliotis, Spyros Makridakis, Vassilios Assimakopoulos (2022). Exploring the representativeness of the M5 competition data, International Journal of Forecasting 38(4): 1500-1506, doi: 10.1016/j.ijforecast.2021.07.006. Online. Working paper.
  17. Thiyanga S. Talagala, Feng Li, Yanfei Kang* (2022). FFORMPP: Feature-based forecast model performance prediction, International Journal of Forecasting 38(3): 920-943, doi: 10.1016/j.ijforecast.2021.07.002. Online. Working paper. R package.
  18. Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir (2021). Improving the accuracy of global forecasting models using time series data augmentation, Pattern Recognition 120:108148, doi: 10.1016/j.patcog.2021.108148. Online. Working paper.
  19. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2021). The uncertainty estimation of feature-based forecast combinations, Journal of the Operational Research Society 73(5): 979-993, doi: 10.1080/01605682.2021.1880297. Online. Working paper. R package.
  20. 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 132: 719-731, doi: 10.1016/j.jbusres.2020.10.051. Online. Working paper. Online app.
  21. Xixi Li, Yanfei Kang, Feng Li (2020). Forecasting with time series imaging, Expert Systems with Applications 160: 113680, doi: 10.1016/j.eswa.2020.113680. Online. Working paper. Code.
  22. 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, doi: 10.1002/sam.11461. Online. Working paper. R package. Shiny app.
  23. Yitian Chen, Yanfei Kang*, Yixiong Chen, Zizhuo Wang (2020). Probabilistic forecasting with temporal convolutional neural network, Neurocomputing 399: 491-501, doi:10.1016/j.neucom.2020.03.011. Online. Code.
  24. Feng Li, Yanfei Kang* (2018). Improving forecasting performance using covariate-dependent copula models, International Journal of Forecasting 34(3): 456-476, doi:10.1016/j.ijforecast.2018.01.007. Online.
  25. Yanfei Kang*, Rob J. Hyndman, Kate Smith-Miles. (2017). Visualising forecasting algorithm performance using time Series instance space. International Journal of Forecasting 33(2): 345–358, doi: 10.1016/j.ijforecast.2016.09.004. Online.
  26. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2015). Classes of structures in the stable atmospheric boundary layer. Quarterly Journal of the Royal Meteorological Society 141(691): 2057–2069, doi: 10.1002/qj.2501. Online. R package.
  27. Yanfei Kang. (2015). Detection, classification and analysis of events in turbulence time series. Bulletin of the Australian Mathematical Society 91(3): 521-522, doi: 10.1017/S0004972715000106. Online.
  28. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2014). Detecting and classifying events in noisy time series. Journal of the Atmospheric Sciences 71(3): 1090–1104, doi: 10.1175/JAS-D-13-0182.1. Online.
  29. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2014). A note on the relationship between turbulent coherent structures and phase correlation. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2) 023114: 1-6, doi: 10.1063/1.4875260. Online.
  30. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2013). How to extract meaningful shapes from noisy time-series subsequences? In: Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, pp. 65–72, doi: 10.1109/CIDM.2013.6597219. Online.
  31. Yanfei Kang. (2012). Real-time change detection in time series based on growing feature quantization. In: Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–6, doi: 10.1109/IJCNN.2012.6252381. Online.

学术兼职

  • International Journal of Forecasting (SSCI, JCR Q1)副主编，2024.09 – 今
  • R Journal (SCI, JCR Q1) 副主编，2023.11 – 今
  • 中国统计教育学会理事
  • 北京大数据协会理事
  • 中国现场统计研究会多元分析应用专业委员会理事
  • 全国工业统计学教学研究会青年统计学家协会理事

学术会议报告

  • The 44th International Symposium on Forecasting, June 2024, France.
  • 2021 ICDM workshop SFE-TSDM: Systematic Feature Engineering for Time-Series Data Mining (keynote), Dec 2021, Virtual. Slides.
  • The 40th International Symposium on Forecasting, Oct 2020, Virtual. Slides.
  • The 12th Conference on Monte Carlo Methods, July 2019, Australia.
  • The 39th International Symposium on Forecasting, June 2019, Greece.
  • The 2017 Beijing Workshop on Forecasting, Nov. 2017, Beijing, China.
  • The 37th International Symposium on Forecasting, June 2017, Australia.
  • The 1st International Conference on Econometrics and Statistics, June 2017, Hongkong.
  • The 7th International Forum on Statistics of Renmin University of China, May 2016, China.
  • The 2014 Conference – Mathematics of Planet Earth (MPE) Australia, October 2014, Australia.
  • The 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), June 2013, Singapore.
  • The 2012 International Joint Conference on Neural Networks (IJCNN), June 2012,  Australia.
  • The 2011 Australian Mathematical Sciences Institute (AMSI) Graduate Winter School,  June 2012, Australia.

合作者