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      
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！

康雁飞博士现任北京航空航天大学经济管理学院副教授、博士生导师。博士毕业于澳大利亚莫纳什大学，师从Kate Smith-Mile教授。之后在澳大利亚科学院院士Rob Hyndman教授指导下完成博士后研究工作。康雁飞博士的研究领域包括时间序列预测、统计计算等领域。先后主持国家自然科学基金青年和面上项目。

康雁飞博士最新研究成果发表在经济与管理学期刊 European Journal of Operational Research, International Journal of Forecasting, Journal of Business Research, Journal of the Operational Research Society, 机器学习与人工智能期刊 Neurocomputing, Pattern Recognition, Expert Systems with Applications，大气科学与物理期刊 Journal of the Atmospheric Sciences, Chaos: An Interdisciplinary Journal of Nonlinear Science 等，同时著有 《大数据分布式存储与计算》和《统计计算》。

康雁飞博士在国际预测大会、ICDM、IJCNN、IEEE CIDM、世界贝叶斯大会等受邀做报告。

教育背景

  • 2010.09 – 2014.08 博士 澳大利亚蒙纳仕什大学
  • 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

统计预测；统计计算；时间序列分析；大数据与机器学习

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 澳大利亚蒙纳仕大学 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.
  3. Yun Bai, Ganglin Tian, Yanfei Kang*, Suling Jia (2021). A hybrid ensemble method with negative correlation learning for regression. Working paper.

学术论文

  1. Xiaoqian Wang, Rob Hyndman, Feng Li, Yanfei Kang* (2022). Forecast combinations: an over 50-year review (in press). International Journal of Forecasting, doi: 10.1016/j.ijforecast.2022.11.005. Online. Working paper.
  2. 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.
  3. Li Li, Yanfei Kang, Fotios Petropoulos, Feng Li (2022). Feature-based intermittent demand forecast combinations: accuracy and inventory implications (in press). International Journal of Production Research, doi: 10.1080/00207543.2022.2153941. Online. Working paper.
  4. Li Li, Yanfei Kang, Feng Li (2022). Bayesian forecast combination using time-varying features (in press). International Journal of Forecasting, doi: 10.1016/j.ijforecast.2022.06.002. Online. Working paper.
  5. Xiaoqian Wang, Yanfei Kang, Rob Hyndman, Feng Li (2022). Distributed ARIMA models for ultra-long time series (in press). International Journal of Forecasting, doi: 10.1016/j.ijforecast.2022.05.001. Online. Working paper. Spark implementation.
  6. Xixi Li, Fotios Petropoulos, Yanfei Kang* (2022). Improving forecasting by subsampling seasonal time series (in press). International Journal of Production Research, doi: 10.1080/00207543.2021.2022800. Online. Working paper.
  7. 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 (in press). International Journal of Forecasting 38(3): 705-871, doi: 10.1016/j.ijforecast.2021.11.001. Online. Working paper. Bookdown version.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 康雁飞、李丰（2019 译）. 预测：方法与实践（第二版）（Hyndman & Athanasopoulos 著. Forecasting: Principles and Practice）. 在线版本.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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

  • 美国国家自然科学基金（NSF）匿名评审人
  • International Journal of Forecasting, Journal of Statistical Software, Proceedings of the Royal Society A 等国际期刊审稿人

学术会议报告

  • 2020年国际预测大会（The 2020 International Symposium on Forecasting），分会场主席，线上
  • 2019年蒙特卡罗方法会议（The 2019 Conference on Monte Carlo Methods），澳大利亚
  • 2019年国际预测大会（The 2019 International Symposium on Forecasting），分会场主席，希腊
  • 2017年国际预测大会（The 2017 International Symposium on Forecasting），分会场主席，澳大利亚
  • 2017年计量经济学与统计学国际会议（The 2017 International Conference on Econometrics and Statistics），邀请报告，中国香港
  • 2016年国际贝叶斯大会（International Society for Bayesian Analysis World Meeting 2016），分组报告，意大利
  • 2014年地球数学会议（The 2014 Conference – Mathematics of Planet Earth），邀请报告，澳大利亚
  • 2013年IEEE计算机智能与数据挖掘大会（The 2013 IEEE Symposium on Computational Intelligence and Data Mining），分组报告，新加坡
  • 2012年IEEE神经网络大会（The 2012 International Joint Conference on Neural Networks），邀请报告，澳大利亚

合作者