November 13, 2026, Webb Hall 1100

Machine learning (ML) models can be effective for forecasting the dynamics of unknown systems from time-series data, but they often require large datasets and struggle to generalize—that is, they fail when applied to systems with dynamics different from those seen during training. Combined, these challenges make forecasting from short time series particularly difficult. To address this, we introduce Meta-learning for Tailored Forecasting from Related Time Series (METAFORS), which supplements limited data from the system of interest with longer time series from systems that are suspected to be related. By leveraging a library of models trained on these potentially related systems, METAFORS builds tailored models to forecast system evolution with limited data. Using a reservoir computing implementation and testing on simulated chaotic systems, we demonstrate METAFORS’ ability to predict both short-term dynamics and long-term statistics, even when test and related systems exhibit significantly different behaviors, highlighting its strengths in data-limited scenarios.

Michelle Girvan is a Professor and Distinguished Scholar-Teacher in the Department of Physics at the University of Maryland (UMD). She also holds appointments in the Institute for Physical Science and Technology, the Institute for Research in Electronics and Applied Physics, and the Applied Math and Scientific Computing Program. Her research focuses on applications of network science to biological, social, and technological systems. Much of her recent work explores the intersection of network science and machine learning.

Girvan received bachelor’s degrees in physics and mathematics from MIT and her PhD in physics from Cornell University. Before joining the University of Maryland faculty, she held a postdoctoral fellowship at the Santa Fe Institute, where she is now a member of the External Faculty. She is President of the Network Science Society and a Fellow of both the American Physical Society and the Network Science Society.