个人简历

教育背景\r
2010.09 – 2014.08 博士 澳大利亚莫纳什大学\r
博士论文: Event detection, classification and analysis on atmospheric time series（导师: Kate Smith-Miles 教授、Danijel Belusic教授）\r
2009.09 – 2010.07 统计学研究生 中国人民大学\r
2005.09 – 2009.07 统计学本科 山东财经大学\r
\r
工作经历\r
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\r
2015.08 – 2016.08 百度大数据部 大数据高级研发工程师\r
2014.08 – 2015.07 澳大利亚莫纳什大学 博士后\r
合作导师: Rob Hyndman 教授；Kate Smith-Miles 教授

研究领域

"Forecasting\r
Statistical Computing\r
Big Data and Machine Learning"

近期论文

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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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., G?nül, 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., ?nkal, 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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.\r
\r
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.
• 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