While there is a growing recognition by agencies, organizations, and governments that data-driven decision-making tools, like forecasting models, can offer significant improvements to the societies they are working to improve, there is not a cohesive body of research that offers guidance on how to best implement, understand, use, and evaluate forecasting methods for societal impact in practice. The goal of this workshop is to improve the research and practice in issues related to forecasting for social good by: facilitating interactions between practitioners, researchers, and policy makers to develop a cohesive and sustainable network of international collaborations with a focus on issues related to forecasting for social good; promoting the development of new methodology and metrics to address the specific challenges related to forecasting for social good; providing professional development to policy makers; gaining a better understanding of the available data, challenges in data acquisition, and the uncertainty present in the data used to produce forecasts; and, addressing the ethical issues related to the use of forecasting methods for problems that impact society.
Buket Cilali, University of Oklahama
Ahmed Aziz Ezzat, Rutgers, The State University of New Jersey
Michael Porter, University of Virginia
Bahman Rostami-Tabar Cardiff University
Feature-based Time Series Forecasting | Ordos City, China | schedule to be announced
Feature-based forecasting aims to use meta-learning to produce more accurate forecasts based on time series features. As early as the 1970s, Adam (1973) argued that the statistical characteristics of time series can be used to improve forecasting performances. Later literature supported this argument. Recently, related feature-based algorithms have been developed with great success, while some key issues remain to be tackled including automatic feature extraction and selection, feature-based density forecasting, forecasting related time series with features, time series generation based on features, and minimum forecast model pools for forecast model averaging. The proposed workshop aims to bring people together with diverse expertise and facilitate new research collaborations.
For more information: workshop webpage or Prof. Yanfei Kang
This is a series of ongoing workshops on forecasting using R in developing countries. The aim of the project is to ‘train the trainers’ in the form of university students, academics and professionals on the principles of forecasting using R software to support decision making. The ultimate goal of this project is to train 400 individuals, over 5 years in 20 developing countries in the world. The first series of workshops have already been offered in 2017-18. To date, the workshops have been offered in Tunisia, Iraq, Senegal, Nigeria, Uganda, Turkey, Georgia, and Indonesia
The workshops are given by Dr. Bahman Rostami-Tabar, Assistant Professor in Management Science at Cardiff Business School. To organise a workshop in your country, contact [email protected]