My Google Scholar Profile
- Thiyanga Talagala, Feng Li, Yanfei Kang (2019). FFORMPP: Feature-based forecast model performance prediction.
- Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li (2019). Que será será? The uncertainty estimation of feature-based time series forecasts. Working paper.
- Yitian Chen, Yanfei Kang, Yixiong Chen, Zizhuo Wang (2019). Probabilistic Forecasting with Temporal Convolutional Neural Network. Working paper.
- Xixi Li, Yanfei Kang, Feng Li (2019). Forecasting with time series imaging. Working paper.
- Yanfei Kang, Rob J Hyndman, Feng Li (2018). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. Working paper. R package. Shiny app.
- 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.
- 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.
- 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.