STATE-SPACE MODELS: UNRAVELLING HIDDEN DYNAMICS IN DATA
P. R Vekariya, Prity Kumari, Yogesh A. Garde · 2023
The chapter encapsulates a sophisticated framework for analyzing complex systems by revealing latent patterns and dynamics within observed data. State-space models (SSMs) have emerged as a fundamental tool in various disciplines, including engineering, economics, biology, and neuroscience, due to their capacity to unearth underlying dynamics that generate observed measurements. SSMs operate on the premise that observed data is a manifestation of both observable states and hidden variables, with a temporal evolution influenced by intricate dynamics. By disentangling these hidden dynamics, SSMs enable researchers to gain deeper insights into the underlying mechanisms that govern system behavior. This is achieved through a two-fold process: the observation model and the state-transition model. The utilization of SSMs is particularly valuable in scenarios where direct measurements of underlying phenomena are unattainable or noisy. In summary, "State-Space Models: Unraveling Hidden Dynamics in Data" underscores the importance of these models as a versatile approach for capturing the intricate relationships between observed data and the latent processes driving it. By harnessing SSMs, researchers are empowered to uncover the concealed dynamics that shape various phenomena, fostering a deeper comprehension of complex systems across diverse domains.