State Space Representation

State space representation is a powerful framework for modeling dynamic systems by describing their evolution through a set of hidden states. Current research focuses on developing more flexible and efficient state space models, including probabilistic approaches to handle uncertainties, hybrid models combining neural networks with traditional time series methods for improved nonlinear prediction, and extensions to handle high-dimensional data like images and graphs. These advancements are improving the accuracy and applicability of state space models across diverse fields, from robotics and control systems to neuroscience and data science, enabling better real-time optimization, fault detection, and prediction in complex systems.