ABSTRACT
Parts I and II laid the theoretical foundations for developing data-driven models for deterministic and random processes. Different forms of linear time-invariant models that can be possibly built were studied. We also obtained glimpses of a few tools that can be used for estimating these models. These tools required the characterization and estimation of a signal’s properties such as auto-covariance function, spectral density, etc. Having learnt the theoretical definitions and characterizations of the signal’s properties, the next natural step in identification is to learn how to estimate these signal properties and model parameters.
