The International Institute of Forecasters is calling for proposals on how to improve forecasting methods and business forecasting practice. This is the 16th year of the financial support from SAS on this IIF-SAS award. In addition to the $10,000 funding from SAS, the IIF is adding another $10,000 to the award pool this year, so that two $10,000 grants are going to be awarded to the best proposals in methodology and practice/management categories.
The applications are due on September 30, 2019. The application must include:
- Description of the project (at most 4 pages)
- C.V./resume (brief, 4 page max)
- Budget and work-plan for the project (brief, 1 page max)
Criteria for the award of the grant will include likely impact on forecasting methods and business applications.
Details about the award can be found from the IIF website. For the frequent readers of this blog and SWEET members, I'm listing the energy related projects that were awarded in the last decade:
- Robust kernel-free nonlinear support vector regression models for load forecasting. Jian Luo, Dongbei University of Finance & Economics, China. (2018-2019 grant, methodology category)
- Hierarchy-based disaggregate forecasting using deep machine learning in power system time series. Cong Feng and Jie Zhang, The University of Texas at Dallas, USA. (2017-2018 grant, business applications category)
- Convolutional neural networks for spatio-temporal wind speed forecasting. Fernando Cyrino and Bruno Q. Bastos, Pontifical Catholic University of Rio de Janeiro, Brazil. (2017-2018 grant, methodology category)
- Short-term load forecasting using rule-based seasonal exponential smoothing incorporating special day effects. Siddharth Arora and James Taylor, University of Oxford, UK. (2010-2011 grant, business applications category)