欢迎访问 KLLAB

欢迎来到康雁飞和李丰老师的共同实验室——KLLAB(发音为[col·lab],意思是合作。一个知识和学习的实验室)。KLLAB 不仅是一个以康和李的首字母命名的实验室,而且还代表着我们实验室里人的知识(knowledge)和学习(learning)。我们的 KLLAB 始于 2016 年初北京航空航天大学和中央财经大学的首次联合讨论班。 截止2020 年,KLLAB 已达到 20 名成员。

我们实验室专注于发现和解决时间序列预测统计计算分布式学习中的有趣问题。

我们的 KLLAB 每年都会邀请合作者参观实验室,我们还组织主题研讨会。我们的研究网络遍及澳大利亚、英国、瑞典、美国和其他国家。请在此页面上查看我们的全球协作网络。

我们欢迎从本科到博士各个层次的大学生加入 KLLAB,我们 还将最优秀的学生推荐到世界一流大学。康雁飞博士和李丰博士还在各个所在大学开设统计计算[K][L]、贝叶斯分析[K][L]、分布式统计计算[L]、和数据科学 [L] 课程,欢迎大家选修。

承担项目

  1. 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,2022年 – 2025年,负责人
  2. 北京航空航天大学“青年拔尖人才支持计划”,2021年 – 2023年,负责人
  3. 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,2021年 – 2022年,负责人
  4. 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,2018年 – 2020年,负责人
  5. 北京航空航天大学“卓越百人计划”,2017年 – 2019年,负责人
  6. 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,2017年 – 2018年,负责人
  7. 国家自然科学基金面上项目(82074282):中医药临床疗效评价中基于目标值法的单臂临床研究方法体系的构建。2021/01-今、项目主要参与人,在研。
  8. 国家自然科学基金青年项目(11501587):贝叶斯柔性密度方法及其在高维金融数据中的应用。2016/01-2018/12、项目负责人
  9. 教育部基金项目:贝叶斯弹性高维密度方法在复杂数据的研究。2014/01-2017/12、项目负责人。
  10. 国家自然科学基金青年项目(11401603):复发事件的均值模型和纵向数据的分位数回归的统计与推断。2015/01-2017/12、参加。
  11. 国家自然科学基金青年项目(71401192):公司财务困境预警模型研究:基于财务波动信息的区间数据刻画方法、2015/01-2017/12、参加。
  12. 国家自然科学基金面上项目(71473279):货币总量转向信用总量:全球虚拟经济与实体经济背离机理与宏观政策应对、2015/01-2017/12、参加。
  13. Stress-testing algorithms: generating new test instances to elicit insights, funded by Australian Research Council,2014–2015.

研究成果

(粗体为实验室成员,#为共同第一作者,*为通讯作者)

  1. Evangelos Theodorou#, Shengjie Wang#Yanfei Kang*, Evangelos Spiliotis, Spyros Makridakis, Vassilios Assimakopoulos (2021). Exploring the representativeness of the M5 competition data. International Journal of Forecasting. (in press).
  2. Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li and Hansheng Wang (2021). A Note on Distributed Quantile Regression by Pilot Sampling and One-Step UpdatingJournal of Business and Economic Statistics. (in press).
  3. Thiyanga S. Talagala, Feng LiYanfei Kang* (2021). FFORMPP: Feature-based forecast model performance predictionInternational Journal of Forecasting. (in press)
  4. Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir (2021). Improving the Accuracy of Global Forecasting Models using Time Series Data AugmentationPattern Recognition. (in press)
  5. Xuening Zhu, Feng Li*, & Hansheng Wang (2021). Least-square approximation for a distributed system. Journal of Computational and Graphical Statistics. (in press).
  6. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, & Feng Li* (2021). The uncertainty estimation of feature-based forecast combinations, Journal of the Operational Research Society. (in press).
  7. Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li*, & Vassilios Assimakopoulo (2021). Déjà vu: A data-centric forecasting approach through time series cross-similarityJournal of Business Research. 132(2021):719-731.
  8. Megan GJaneway, Xiang Zhao, Max Rosenthaler, Yi Zuo, Kumar Balasubramaniyane, Michael Poulson, Miriam Neufeld, Jeffrey J. Siracuse, Courtney E. Takahashif, Lisa, Allee, Tracey Dechert, Peter A Burke, Feng Li, and Bindu Kalesan (2021). Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injuryJournal of Affective Disorders 278(1):172-180.
  9. Yitian Chen, Yanfei Kang*, Yixiong Chen, Zizhuo Wang (2020). Probabilistic Forecasting with Temporal Convolutional Neural NetworkNeurocomputing 399: 491-501.
  10. Xixi Li, Yanfei Kang,  & Feng Li* (2020). Forecasting with time series imagingExpert Systems with Applications 160: 113680.
  11. Yanfei Kang, Rob J Hyndman,  & Feng Li* (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsStatistical Analysis and Data Mining 13(4): 354-376.
  12. Chengcheng Hao, Feng Li,  & Dietrich von Rosen (2020). A Bilinear Reduced Rank ModelIn Jianqing Fan and Jianxin Pan (eds.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer.
  13. Bindu Kalesan, Siran Zhao, Michael Poulson, Miriam Neufeld, Tracey Dechert, Jeffrey J Siracuse, Yi Zuo, and Feng Li (2020). Intersections between firearm suicide, drug mortality and economic dependency in rural AmericaJournal of Surgical Research. 256, pp 96-102. Journal’s Cover Paper.
  14. Hannah M Bailey, Yi Zuo, Feng Li, Jae Min, Krishna Vaddiparti, Mattia Prosperi, Jeffrey Fagan, Sandro Galea,  & Bindu Kalesan (2019). Changes in patterns of mortality rates and years of life lost due to firearms in the united states,1999 to 2016: A joinpoint analysisPLoS One, 14(11).
  15. Feng Li  & Zhuojing He (2019). Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? Cogent Economics & Finance, page 1632528.
  16. Feng Li & Yanfei Kang* (2018). Improving forecasting performance using covariate-dependent copula models. International Journal of Forecasting, 34(3):456–476.
  17. Elizabeth C Pino, Yi Zuo, Camila Maciel DeOlivera, Shruthi Mahalingaiah, Olivia Keiser, Lynn L Moore, Feng Li, Ramachandran S Vasan, Barbara E Corkey  & Bindu Kalesan (2018). Cohort profile: The multistudy diabetes research (multitude) consortiumBMJ Open, 8(5):e020640.
  18. Yanfei Kang*, Rob J. Hyndman, Kate Smith-Miles. (2017). Visualising Forecasting Algorithm Performance using Time Series Instance SpaceInternational Journal of Forecasting 33(2): 345–358.
  19. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2015). Classes of Structures in the Stable Atmospheric Boundary LayerQuarterly Journal of the Royal Meteorological Society 141(691): 2057–2069. 
  20. Yanfei Kang. (2015). Detection, classification and analysis of events in turbulence time seriesBulletin of the Australian Mathematical Society 91(3): 521-522.
  21. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2014). Detecting and classifying events in noisy time seriesJournal of the Atmospheric Sciences 71(3): 1090–1104.
  22. Yanfei Kang, Danijel Belusic, Kate Smith-Miles. (2014). A note on the relationship between turbulent coherent structures and phase correlationChaos: An Interdisciplinary Journal of Nonlinear Science 24(2) 023114: 1-6.
  23. Feng Li (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1.
  24. Feng Li  & Mattias Villani (2013). Efficient Bayesian multivariate surface regressionScandinavian Journal of Statistics, 40(4):706–723.
  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.
  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.
  27. Feng Li, Mattias Villani  & Robert Kohn (2011.). Modeling conditional densities using finite smooth mixtures. In Kerrie Mengersen, Christian Robert, Mike Titterington (eds.), Mixtures: estimation and applications, pages 123–144. John Wiley & Sons Inc, Chichester.
  28. Feng Li, Mattias Villani  & Robert Kohn (2010). Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densitiesJournal of Statistical Planning and Inference,140(12):3638–3654

专著与教材

  1. Hyndman, R.J., & Athanasopoulos, G.著. 预测:方法与实践(第2版),康雁飞李丰(译)https://otexts.com/fppcn/
  2. 李丰(2016)大数据分布式计算与案例。中国人民大学出版社。ISBN 9787300230276. [ 第二版在线预览 ]
  3. 康雁飞李丰(2021)统计计算。[ 在线预览版本 ]