Forecasting Impact
Forecasting Impact is a bimonthly podcast that aims to disseminate the science and practice of forecasting by introducing prominent academics, practitioners, and visionaries in the forecasting domain. Our vision is to help grow the forecasting community, foster collaboration between academia, industry, and governments, and promote scientific forecasting and good practices.
We will discuss a range of forecasting topics in economics, supply chain, energy, social goods, AI, machine learning, data analytics, education, healthcare, and more.
Forecasting Impact episodes are also available on the IIF YouTube Channel @IIForecasters.
Podcast Team
Chair and Co-host: Dr. Laila Ahadi-Akhlaghi, Senior Technical Advisor at JSI.
Additional co-hosts:
- Dr. Mahdi Abolghasemi, Lecturer in Data Science at The University of Queensland,
- George Boretos, Founder & CEO at FutureUP,
- Dr. Faranak Golestaneh, Data Science Senior Manager at Commonwealth Bank of Australia,
- Mariana Menchero, Senior Forecaster at Nixtla, and
- Arian Sultan Khan, Data Analyst at VAN
Co-hosts in the past have included: Michał Chojnowski, Shari De Baets, Elaine Deschamps, Dr. Sevvandi Kandanaarachchi, Bahman Rostami-Tabar, Anna Sroginis, and Sarah Van der Auweraer.
We welcome your feedback, questions, and suggestions. Please contact us at forecastingimpact@forecasters.org
Forecasting Impact
Michele Trovero and Spiros Potamitis, on Software and Large Language Models in Forecasting
Our guests are Michele Trovero, leader of the Forecasting R&D group at SAS, and Spiros Potamitis, Data Scientist and Product Marketing Manager at SAS. We delved into the intriguing intersection of Language Model-based AI (LLMs) and forecasting software.
We explored the openness of forecasting software providers to embrace LLMs and discussed the profound impact these models could have on the industry. Michele and Spiros shared insightful examples of LLM applications. They elaborated on the way code generation capabilities powered by LLMs would enhance the development of forecasting software and the user experience. Additionally, they explored how LLMs could democratize forecasting, and discussed other tools and technologies that could contribute to this goal. We also discussed the typology of models behind LLMs, and their applicability in forecasting, as well as the limitations and enablers in using AI-pretrained models in forecasting. The discussion extended to SAS Visual Forecasting and Model Studio, shedding light on their functionalities and workings.
Michele and Spiros speculated on the areas of focus for forecasting software companies, enhanced automation in forecasting, shifts in user consumption patterns, and anticipated integrations between forecasting systems and other technologies.
They recommended the following for further study:
1. How Will Generative AI Influence Forecasting Software? by Michele Trovero and Spiros Potamitis, Foresight: The International Journal of Applied Forecasting.
2. A Glimpse into the Future of Forecasting Software, by Spiros Potamitis, Michele Trovero, Joe Katz, Foresight: The International Journal of Applied Forecasting.