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.

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/ramikrispin
LinkedIn Page: https://www.linkedin.com/in/rami-krispin/

In this episode, Dr. Laila Akhlaghi and Professor Bahman Rostami-Tabar host a discussion on healthcare product forecasting in Eastern Africa with Harrison Mariki from Tanzania and Danielson Kennedy Onyango from Kenya. Harrison, founder of Afya Intelligence, discusses leveraging AI to improve forecasting for 7,000 primary healthcare facilities in Tanzania, addressing data quality and supply chain challenges. Ken, from inSupply Health, highlights the use of open-source tools and human-centered design to enhance forecasting accuracy and efficiency in Kenya. Both emphasize the importance of local talent, trust, and co-creation in developing effective forecasting solutions.

In this episode of Forecasting Impact, we had the privilege of hosting Professor Jan Kleissl, a leading expert in solar power forecasting. Professor Kleissl began by sharing his journey into solar forecasting, emphasizing the growing importance of renewable energy in addressing climate change. He explained the critical role solar forecasting plays in balancing energy grids, ensuring reliability, and integrating renewable energy at scale.

The discussion delved into the technical challenges of forecasting, such as dealing with weather variability, and explored the challenges ahead for NET Zero and power transformation in the field. We further explored the practical applications of accurate solar forecasting, with Professor Kleissl highlighting how it benefits grid operators and consumers by optimizing energy distribution and reducing costs. He shared insights into decentralized energy resources and fostering innovation.

He recommended the following paper for readers interested in solar power forecasting and its role in the energy market: The Value of Day-Ahead Solar Power Forecasting Improvement, published in Solar Energy.

In this episode, hosts Arian Sultan and Laila Akhlaghi discuss financial tools that enable healthcare markets to function more efficiently and how forecasting plays an important role in their execution with Hema Srinivasan of MedAccess. Hema Srinivasan is a senior advisor to MedAccess, supporting work to identify and execute opportunities for financial tools to help lower prices and increase the availability of medical products. She supports the Health Markets team in sourcing and developing pipeline opportunities for the deployment of MedAccess’ tools, managing the monitoring and implementation of transactions post-execution, and analyzing development impact throughout the partnership development and implementation process.

This episode explores her career and how she has used forecasting to develop market-shaping mechanisms and the methodologies that have led to increases in access to life-saving medical products. This includes analyzing market failures, identifying leverage points for intervention, and implementing policies or programs to rectify imbalances. The episode discusses how these interventions can lead to sustainable and scalable impacts, particularly in sectors where market inefficiencies hinder progress. It highlights interventions that lower prices and increase access to pharmaceuticals, diagnostics, and other medical products in low and middle-income countries (LMICs).

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.

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.

In this episode, we discuss 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.

In this episode, guest hosts George Boretos and Arian Sultan Khan explore the intersection of Neuroscience with Forecasting & AI with guest Kai Markus Mueller, acclaimed neuroscientist and a pioneer in Neuropricing.

Kai, who began his journey in psychology with aspirations of becoming a child psychotherapist, eventually shifted his focus to cognitive psychology and neuroscience. His transition from academia to the industry led to the invention of Neuropricing that utilizes fMRI and EEG to understand consumer behavior and predict responses to advertising and pricing.

The podcast delves into Kai’s innovative work, highlighting how brain activity can often predict consumer behavior more accurately than traditional self-reported methods, with success stories such as Starbucks coffee pricing research and Pepsi’s strategy in Turkey.

Kai explains the practical applications of neuroscience in business, such as storyboard testing for advertising effectiveness. He discusses the integration of AI with neuroscience to enhance predictive models.

He also shares insights on balancing his various roles as an entrepreneur, professor, and industry practitioner, emphasizing the importance of a supportive team. Looking ahead, Kai sees immense potential for neuroscience and AI to transform business strategies, pricing, and drive marketing success.

The conversation underscores the growing mainstream acceptance and practical benefits of these advanced technologies.