2017 Beijing Workshop on Forecasting
Location: Central University of Finance and Economics, Beijing, China.
Slides
Textbook
Relevant papers
- Rob J Hyndman, Anne B Koehler, Ralph D Snyder, Simone Grose (2002) A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting 18(3), 439-454.
- Rob J Hyndman, Anne B Koehler (2006) Another look at measures of forecast accuracy. International Journal of Forecasting 22(4), 679-688.
- Rob J Hyndman, Yeasmin Khandakar (2008) Automatic time series forecasting: the forecast package for R. Journal of Statistical Software 27(3).
- Rob J Hyndman, Roman A. Ahmed, George Athanasopoulos, Han L Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis 55(9), 2579-2589
- Alysha M De Livera, Rob J Hyndman, Ralph D Snyder (2011) Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association 106(496), 1513-1527.
- Tilmann Gneiting, Matthias Katzfuss (2014) Probabilistic forecasting, Annual Review of Statistics and its Applications, 1, 125-151.
- Shanika L Wickramasuriya, George Athanasopoulos, Rob J Hyndman (2015) Forecasting hierarchical and grouped time series through trace minimization.
- Rob J Hyndman, Alan Lee, Earo Wang (2016) Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis 97, 16-32.
- George Athanasopoulos, Rob J Hyndman, Nikolaos Kourentzes, Fotios Petropoulos (2017) Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1) 60–74.
- Souhaib Ben Taieb, James W Taylor, Rob J Hyndman (2017) Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data.
Additional collaborators
These talks contain many ideas from my coauthors and students. Apart from the coauthors in the papers listed above, the following people have contributed ideas (and I hope they will be coauthors of future papers):