Presentation Information
[PT01-01]Empirical Dynamic Modeling toward mechanistic understanding and forecasting complex systems
*Chih hao Hsieh1 (1. Institute of Oceanography, National Taiwan University (Taiwan))
Keywords:
Time-series Analysis
Mechanistic understanding and forecasting are important for effective system policy and management recommendations. However, these tasks are challenging because the real world is complex, where correlation does not necessarily imply causation. Here, I present a time-series analytical framework, known as Empirical Dynamic Modeling (EDM). EDM enables detection of causality among interacting components in nonlinear dynamical systems, construction of time-varying interaction networks, forecasting of effects of external forcing, and provision of early warning signals for critical transition. I will demonstrate the efficacy of EDM in various systems, including environmental change effects on aquatic ecosystems, fisheries, climate, and bioreactor for clean energy. The information can shed light on identifying drivers of system stability and translating this science into policy-relevant information.