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
Panel on Foundational Models with Azul Garza Ramírez and Mononito Goswami- Part 2
In this episode, hosts Mariana Menchero and Faranak Golestaneh explore the cutting-edge world of foundation models for time series forecasting with guests Azul Garza Ramírez, cofounder of Nixtla, and Mononito Goswami, one of the developers of MOMENT, a family of open-source foundation models for general-purpose time series analysis. The conversation delves into the backgrounds of these innovators and their journey into the realm of time series analysis and forecasting.
The podcast explores the guests' transition into working with foundation models for time series forecasting. The guests describe the empirical approach they took, inspired by the success of Transformers in other domains like video, images, and text. Their experiments with adapting these models to time series data yielded exciting results, leading to the development of new products and tools.
The conversation sets the stage for a deep dive into the challenges and opportunities presented by foundation models in time series forecasting. The discussion highlights the need for massive, diverse datasets and the potential for these models to learn patterns and extrapolate to new data effectively.
This episode underscores the rapid advancements in time series forecasting and the growing importance of foundation models in pushing the boundaries of what's possible in this field. It offers listeners a glimpse into the minds of innovators who are shaping the future of time series analysis and its applications across various industries.