The Future of AI-Driven Time Series Forecasting (2) AI Is Transforming Business Forecasting - The Rise of Foundation Models for Time Series Forecasting
AI & Data Innovation
TalkSession Code
Sess-103Day 3
14:45 - 15:15 EST
About the Session
Over the last year, we’ve seen an explosion in innovation in time-series forecasting. Along with new statistical models, transformer-based approaches have allowed for the creation of zero-shot foundation models from well-known organizations such as Google, Amazon, and Microsoft and from companies specializing in time-series forecasting such as Nixtla. The pre-trained models make time-series forecasting more accessible and available, especially to smaller organizations with limited resources. If the promises of foundation models materialize, they can revolutionize how practitioners tackle their forecasting tasks. How will this emergence of zero-shot models impact the field of time series forecasting? This session will discuss forecasts in the forecasting field including how: Foundational models will slowly replace other methods for most practical use cases. We will see a growing family of forecasting models with different strengths and weaknesses (performance, size, speed, specialization). Foundation models will democratize access to forecasting and anomaly detection, causing a rise in the number of users. (2) Forecasting is a critical function for businesses of all sizes, from large enterprises to small startups. This session explores how AI-driven time series forecasting is transforming decision-making by enabling businesses to leverage their existing data for accurate, efficient predictions. We will discuss how companies—from Fortune 500 corporations to SMBs—are using AI-powered forecasting to optimize operations, anticipate demand, and prevent costly failures. Real-world case studies, including a global manufacturing firm detecting system faults through AI-based monitoring, will showcase the tangible benefits of AI-driven forecasting. Attendees will learn how to apply AI forecasting models to their own business data while addressing challenges such as data integration and accuracy improvement.