IIF-SAS Grant to Promote Research on Forecasting

For the eighteenth year, the IIF, in collaboration and with financial support from SAS®, is proud to announce financial support for research on how to improve forecasting methods and business forecasting practice. The awards for the 2021 year will be (2) $10,000 grants; in Business Applications and Methodology. The application deadline was 15 October 2021.

For more details, click here IIF-SAS award. Applications must include:

  • Description of the project (4 page max)
  • Brief c.v., including references (4 page max)
  • Budget and work-plan for the project (1 page max)

All applications must be in pdf format and sent to IIF Business Director

Award Recipients

2021

Shanika L Wickramasuriya, University of Auckland; Kasun Bandara, University of Melbourne; Hansika Hewamalage, RMIT University, for the project proposal in the category of Methodology Forecasting Hierarchical Time Series using Non-linear Mappings

Jean-François Toubeau, University of Mons; Yi Wang, University of Hong-Kong, for the project proposal in the category of Applications, Privacy-Preserving Renewable Energy Probabilistic Forecasting

2020

Hussain Syed Kazmi, KU Leuven; Maria Paskevich, King (Activision-Blizzard), for the project proposal in the category of Business Applications, Incorporating downstream, task-specific information in forecasting models

Ahmed Aziz​ Ezzat, Rutgers University, for the project proposal in the category of Methodology, Forecasting in Unknown Territory: Towards Physically Motivated Learning for Local Wind Fields

2019

Katie McConky and Omar Aponte, Rochester Institute of Technology, for the project proposal in the category of Business Applications, Actionable Peak Electric Load Day Forecasting Method for Facilities with Renewable Electricity Cogeneration

Xiaochun Liu, University of Alabama, for the project proposal in the category of Methodology, Conditional Encompassing Test for Value-at-Risk and Expected Shortfall Forecasts: A GMM Approach

2018

Arnaud Dufays, Université Namur, and David Ardia, HEC Montréal, for the project proposal in the category of Business Applications, Benchmark-testing volatility models with an open-source database

Jian Luo, Dongbei University of Finance & Economics, for the project proposal in the category of Methodology, Robust kernel-free nonlinear support vector regression models for load forecasting

2017

Cong Feng and Jie Zhang, The University of Texas at Dallas, for the project proposal in the category of Business Applications Hierarchy-based Disaggregate Forecasting Using Deep Machine Learning in Power System Time Series

Fernando Cyrino and Bruno Q. Bastos, Pontifical Catholic University of Rio de Janeiro, for the project proposal in the category of Methodology Forecasting Wind and Solar Time Series with Convolutional Neural Networks Approach to Spatio-Temporal [IJF publication: https://www.sciencedirect.com/science/article/abs/pii/S0169207020301618]

2016

Julie Novak and Beatriz Etchegaray García, IBM Thomas J. Watson Research Center, for the project proposal in the category of Business Applications A Bayesian Model for Forecasting Time Series in a Hierarchically Structured Organisation

Leopoldo Catania and Tommaso Proietti, University of Rome, for the project proposal in the category of Methodology Forecasting Realized Volatility and the Role of Time Varying Dependence with Market Returns

2015

Jaqueson K. Galimberti, ETH Zürich-KOF Swiss Economic Institute, for the project proposal in the category of Business Applications An outer space view of the business cycles

Tatevik Sekhposyan, Texas A&M University, for the project proposal in the category of Methodology Model Selection and Model Averaging based on Economic Fundamentals

2014

Valerio Poti, University College Dublin, for the project proposal in the category of Business Applications Predicting Predictability

David Ardia, University Laval; Lennart F. Hoogerheide, Vrije Universiteit Amsterdam; Jeremy Kolly, University Laval and Fribourg University, for the project proposal in the category of Methodology Bayesian Prediction of Market Risk using Regime-Switching GARCH Models

2013

Jeffrey Stonebraker, North Carolina State University, for the project proposal in the category of Business Applications Probabilistic Forecasting of the Global Demand for the Treatment of Hemophilia B

Yongchen (Herbert) Zhao, University at Albany, for the project proposal in the category of Methodology Robust Real-Time Automated Forecast Combination in SAS: Development of a SAS Procedure and a Comprehensive Evaluation of Recently Developed Combination Methods

2012

Zoe Theocharis, University College London; Nigel Harvey, University College London; Leonard Smith, London School of Economics, for the project proposal in the category of Business Applications Improving judgmental input to hurricane forecasts in the insurance and reinsurance sector

Yorghos Tripodis, Boston University, for the project proposal in the category of Business Applications Forecasting the Cognitive Status in an Aging Population

Elena-Ivona Dumitrescu, Janine Christine Balter, and Peter Reinhard Hansen, European University Institute, for the project proposal in the category of Methodology Forecasting Exchange Rate Volatility: Multivariate Realized GARCH Framework

2011

Siddharth Arora and James Taylor, University of Oxford, for the project proposal in the category of Business Applications Short-term Load Forecasting Using Rule-based Seasonal Exponential Smoothing Incorporating Special Day Effects

Stavros Asimakopoulos, Lancaster University, for the project proposal in the category of Business Applications How to design Mobile Forecasting User Interfaces (MFUI) to improve business forecasting

Juan Trapero, Universidad de Castilla-La Mancha; Nikolaos Kourentzes, Lancaster University; and Diego Pedregal, Universidad de Castilla-La Mancha, for the project proposal in the category of Methodology Minimizing the gap between judgmental and statistical forecasting in the presence of promotions

2010

Matthias Seifert, IE Business School, for the project proposal in the category of Business Applications Time series forecasting: The contribution of task, decision support and cognitive factors in judgmental effectiveness

Bryan Routledge and Noah Smith, Carnegie Mellon University, for the project proposal in the category of Methodology Text-Driven Forecasting of Mergers: Identifying Targets and Acquirers

2009

Robert Fildes, Lancaster University; and Paul Goodwin, University of Bath, for the project proposal in the category of Business Applications Improving the use of information and advice in supply chain forecasting

David Dickey and Melinda Thielbar, North Carolina State University, for the project proposal in the category of Methodology Neural Networks for Time Series Prediction: Practical Implications of Theoretical Results

2008

Hossein HassaniAnatoly Zhigljavsky and Saeed Heravi, Cardiff University, for the project proposal in the category of Applications Forecasting European Industrial Production with Multivariate Singular Spectrum Analysis

2007

Eric Bélanger, McGill University; Michael S. Lewis-Beck, University of Iowa; and Richard Nadeau, University of Montreal, for their project proposal Improving Election Forecasting in the United Kingdom

Gloria González-Rivera, University of California – Riverside, for the project proposal Evaluation of Multidimensional Predictive Densities

2006

Carolina García-Martos, María Jesús Sánchez and Julio Rodríguez, Universidad Politécnica de Madrid, Unobserved Component Model for Forecasting Electricity Prices and Their Volatilities

Barbara Rossi, Duke University, New Methods for Forecasting and Model Evaluation

2005

Sven Crone, Lancaster University; and Konstantinos Nikolopoulos, Manchester Business School, Automatic Modeling and Forecasting with Neural Networks – A Forecasting Competition Evaluation

Pilar Poncela, Universidad Autónoma de Madrid; and Eva Senra, Universidad de Alcala, Combining Forecasts Through Factor Models: Assessing Consensus and Uncertainty

2004

Peter Zhang, Georgia State University; and Douglas M. Kline, University of North Carolina at Wilmington, Quarterly Times Series Forecasting

Ting Yu, University of Technology at Sydney, Incorporating Prior Domain Knowledge in Machine Learning

2003

Kesten Green, Monash University, Assessing Probabilistic Forecasts

Brajendra Sutradhar, Memorial University of Newfoundland, Best Practice Recommendation for Forecasting Counts

Min Qi, Kent State University; and Peter Zhang, Georgia State University, Trend Time Series Modeling and Forecasting with Neural Networks