How does Kalman filtering work in time series prediction?

Kalman filtering recursively estimates hidden states in noisy dynamic systems using prediction and correction steps.

Kalman filter operates in two steps: prediction (estimating next state based on model) and update (correcting estimate using observed data). It assumes linear dynamics and Gaussian noise. It is optimal for minimizing mean squared error in such conditions. It is widely used in tracking and signal processing.