System models provide a concise description for random signals. In this lesson you will learn how to model a time series as the output of a linear time-invariant system with a random input signal. You will learn about the three commonly used models: autoregressive, moving average, and autoregressive moving average, and the types of random signals they best characterize. This knowledge will prepare you for signal-processing applications to speech coding, spectrum estimation, and system identification.
- The Power Spectral Density
- Introduction to the System Function and System Poles and Zeros
- Recommended: Frequency Response Magnitude and Poles and Zeros