北京航空航天大学经济管理学院
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
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康雁飞,现任北京航空航天大学经济管理学院教授、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
- 2026年 – 2029年,国家自然科学基金面上项目:决策驱动的时间序列预测研究,负责人
- 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
- Xiaorui Luo, Yanfei Kang, Xue Luo (2025), Probabilistic combination forecasts based on particle filtering: predictive prior. Working paper.
- Xiaoqian Wang, Yanfei Kang, Feng Li (2022). Another look at forecast trimming for combinations: robustness, accuracy and diversity. Working paper.
学术论文
- Bohan Zhang, Anastasios Panagiotelis, Yanfei Kang* (2023). Discrete forecast reconciliation. European Journal of Operational Research 318(1): 143-153. Online. Working paper.
- 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.
- 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.
- Spyros Makridakis, Fotios Petropoulos, Yanfei Kang* (2023). Large Language Models: Their success and impact. Forecasting 5(3), 536-549, doi: 10.3390/forecast5030030. Online.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- Prof. Kate Smith-Miles, University of Melbourne
- Prof. Rob J. Hyndman, Monash University
- Dr. Danijel Belusic, Swedish Meteorological and Hydrological Institute
- Prof. Feng Li, Guanghua School of Management, Peking University
- Prof. Fotios Petropoulos, University of Bath
- Prof. Anastasios Panagiotelis, University of Sydney
- Dr. Evangelos Spiliotis, National Technical University of Athens
- Dr. Christoph Bergmeir, Monash University
- Prof. Zizhuo Wang, The Chinese University of Hong Kong
- Dr. Thiyanga Talagala, University of Sri Jayewardenepura
- Yitian Chen, BIGO Technology
- Xixi Li, The University of Manchester