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 in the Department of Physics at the University of Maryland (UMD). She also has 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 is aimed at the intersection of network science and machine learning. Professor Girvan serves as Director and PI of the COMBINE program in Network Biology. COMBINE, which stands for Computation and Mathematics for Biological Networks, is an NSF-funded Research Traineeship program at the University of Maryland which provides interdisciplinary research and training opportunities to graduate students. Professor Girvan received bachelors degrees in physics and mathematics from MIT. She then went on to earn her PhD in physics from Cornell University followed by a postdoctoral fellowship at the Santa Fe Institute before joining the faculty at the University of Maryland. She now serves on the Santa Fe Institute’s External Faculty and Science Steering Committee. Professor Girvan is also a Vice President of the Network Science Society and a Fellow of the American Physical Society. Other past affiliations include the London Mathematical Laboratory and the Institute for Advanced Study.
Michelle Girvan is a Professor in the Department of Physics at the University of Maryland (UMD). She also has 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 is aimed at the intersection of network science and machine learning. Professor Girvan serves as Director and PI of the COMBINE program in Network Biology. COMBINE, which stands for Computation and Mathematics for Biological Networks, is an NSF-funded Research Traineeship program at the University of Maryland which provides interdisciplinary research and training opportunities to graduate students. Professor Girvan received bachelors degrees in physics and mathematics from MIT. She then went on to earn her PhD in physics from Cornell University followed by a postdoctoral fellowship at the Santa Fe Institute before joining the faculty at the University of Maryland. She now serves on the Santa Fe Institute’s External Faculty and Science Steering Committee. Professor Girvan is also a Vice President of the Network Science Society and a Fellow of the American Physical Society. Other past affiliations include the London Mathematical Laboratory and the Institute for Advanced Study.
