My Google Scholar Profile

Working Papers

  1. Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulos (2019). Déjà vu: forecasting with similarity (under review). Working paper.
  2. Thiyanga Talagala, Feng Li, Yanfei Kang (2019). FFORMPP: Feature-based forecast model performance prediction (under review). Working paper.
  3. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2019). Que será será? The uncertainty estimation of feature-based time series forecasts (under review). Working paper.


  1. Xixi Li, Yanfei Kang, Feng Li (2020). Forecasting with time series imaging (in press), Expert Systems with Applications, doi: 10.1016/j.eswa.2020.113680. Online. Working paper.
  2. Yanfei Kang, Rob J Hyndman, Feng Li (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics (in press), Statistical Analysis and Data Mining, doi: 10.1002/sam.11461. Online. Working paper. R package. Shiny app.
  3. Yitian Chen, Yanfei Kang*, Yixiong Chen, Zizhuo Wang (2020). Probabilistic Forecasting with Temporal Convolutional Neural Network (in press), Neurocomputing, doi:10.1016/j.neucom.2020.03.011. Online.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.

Conference Presentations

  • The 12th Conference on Monte Carlo Methods, July 2019, Australia.
  • The 39th Symposium on Forecasting, June 2019, Greece.
  • The 2017 Beijing Workshop on Forecasting, Nov. 2017, Beijing China.
  • The 37th 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.

Research Collaborators