What is state space modeling in time series analysis?

State space models represent time series using hidden states that evolve over time and generate observations.

State space models consist of two equations: a transition equation (hidden state evolution) and an observation equation (linking states to observed data). They are highly flexible and can model noise, trends, and seasonality. Kalman filters are commonly used for inference in linear Gaussian state space models.