康雁飞

北京航空航天大学经济管理学院      
“卓越百人计划”副教授、博士生导师 
地址：北京市海淀区学院路37号新主楼A931 

邮件：yanfeikang@buaa.edu.cn 
主页：http://yanfei.site 
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！

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

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 澳大利亚莫纳什大学 博士后

科研项目

  • 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”启动项目，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）

工作论文

  1. 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. (2020). Forecasting: theory and practice. Working paper.
  2. Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir (2020). Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation (under review). Working paper.
  3. Xiaoqian Wang, Yanfei Kang, Rob Hyndman, Feng Li (2020). Distributed ARIMA models for ultra-long time series (under review). Working paper. Spark implementation.
  4. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2020). Que será será? The uncertainty estimation of feature-based time series forecasts  (under review). Working paper.
  5. Thiyanga S. Talagala, Feng Li, Yanfei Kang (2019). FFORMPP: Feature-based forecast model performance prediction (under review). Working paper. R package.

学术论文

  1. 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. Online. Working paper. Online app.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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