Forecasting Impact

Prediction Markets with Alex McCullough

In this episode of Forecasting Impact, hosts Mariana Menchero and Laila Akhlaghi speak with Alex McCullough about his research on prediction markets, probabilistic forecasting, and the crypto ecosystem. He is best known for his work analyzing the accuracy of Polymarket, where he has built widely referenced dashboards to evaluate market calibration and performance. Through his analysis, Alex helps bring rigor and transparency to the discussion around how prediction markets perform as forecasting tools. Alex's work includes the following dashboards: Initial dashboard: https://dune.com/alexmccullough/how-accurate-is-polymarketAnd the widely referenced Brier Score dashboard: https://dune.com/alexmccullough/polymarket-brier-score

Explainable Forecasting with Marco Peixeiro

In this episode of Forecasting Impact, hosts Mahdi Abolghasemi and Mariana Menchero speak with Marco Peixeiro, applied data scientist at Nixtla, about the growing importance of explainability in time series forecasting. Marco shares how his work bridges research and practice, from developing deep learning models in NeuralForecast to writing educational resources that make complex forecasting concepts accessible to all.We discuss how explainability builds trust in complex models, the role of SHAP values in understanding forecasts, and what the future holds for interpretability in forecasting. Marco also gives us a preview of his upcoming book, Time Series Forecasting Using Foundation Models, exploring how foundation models are transforming the forecasting landscape.Marco’s books, including the upcoming Time Series Forecasting Using Foundations Models: https://www.manning.com/authors/marco-peixeiro

Forecasting the future of everything with Dr. Theodore Modis

In this episode of Forecasting Impact, host George Boretos speaks with Dr. Theodore Modis, acclaimed forecasting expert, strategist, and founder of Growth Dynamics, about the science behind forecasting and the natural laws that govern technological and societal change. From particle physics at CERN to pioneering the use of S-curves in business and technology forecasting, Dr. Modis reveals how principles from physics can reveal powerful insights into product life cycles, market disruptions, and even the future of humankind.Whether you're a strategist, data scientist, or just forecasting-curious, this episode offers deep insight into the future, backed by science.Listen now to discover how forecasting isn't just math but also intuition and foresight combined!A slide deck of the visuals used in this conversation can be accessed here. 

Food Bank Forecasting with Professor Lauren B. Davis

This episode of Forecasting Impact features Professor Lauren B. Davis discussing her research on applying stochastic modeling and forecasting to food bank operations. Lauren shares how she began forecasting with a local food bank, which led her to focus on forecasting the highly uncertain supply of food donations. She details the food banks' donation sourcing process, the management of their supply chains, and the application of models like exponential smoothing, support vector regression, and ensemble methods to predict donation volumes.Professor Davis addresses challenges in forecasting at various aggregation levels (network vs. location-specific), using optimization models for equitable allocation of limited supply, and the significance of storage and agency capacity limits. She emphasizes the importance of equity as an objective, the complexity of modeling true demand, and the crucial role of visual analytics and co-design with food bank partners. The episode underscores the practical impact of forecasting in humanitarian supply chains and the necessity of linking models with operational decisions.

MLOps and Dockerisation in Forecasting with Rami Krispin

In this episode, we sit down with Rami Krispin, a data scientist at Apple and active producer in forecasting, to explore his journey into forecasting and data science. He shares what first sparked his interest in the field and how that passion led him to develop key contributions, including the Hands-On Time Series Analysis with R book and the TSstudio package. We discuss his motivation for writing the book, who it’s for, and how TSstudio and other R packages he has developed have helped practitioners in the forecasting space. He also gives us a sneak peek into his upcoming book, Applied Time Series Analysis and Forecasting with R, and the new topics it will cover.We then dive into the challenges of deploying forecasting models at scale and the role of MLOps in making machine learning projects production-ready. As a Docker Captain, our guest explains how Docker has changed his approach to time series forecasting and MLOps. We also discuss best practices for forecasting, common mistakes practitioners make, and strategies for improving reproducibility. Looking ahead, we talk about where time series forecasting is heading, the differences between R, Julia, and Python in this space, and how each ecosystem serves different needs. You can follow his work on LinkedIn, subscribe to his newsletter, and stay updated on his latest projects.Website: https://linktr.ee/ramikrispinLinkedIn Page: https://www.linkedin.com/in/rami-krispin/