My Google Scholar Profile

Working papers under review

  1. Xixi Li, Fotios Petropoulos, Yanfei Kang* (2021). Improving forecasting with sub-seasonal time series patterns. Working paper.
  2. Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li (2020). Forecast with forecasts: Diversity matters. Working paper.
  3. 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.
  4. Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir (2020). Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation. Working paper.
  5. Xiaoqian Wang, Yanfei Kang, Rob Hyndman, Feng Li (2020). Distributed ARIMA models for ultra-long time series. Working paper. Spark implementation.
  6. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2020). The uncertainty estimation of feature-based forecast combinations. Working paper.
  7. Thiyanga S. Talagala, Feng Li, Yanfei Kang (2019). FFORMPP: Feature-based forecast model performance prediction. Working paper. R package.


  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.

Conference Presentations

  • The 40th Symposium on Forecasting, Oct 2020, Virtual. Slides.
  • 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