Prerequisites: Consent of instructor.

This course is cross-listed with PSY-P657

In the first Time Series I course in Fall 2008, we learned about time
series from a dynamical systems and discrete time perspective.  In
this course, we will build on these skills by approaching time series
analysis from a primarily spectral or frequency-based approach.

Topics will include:

Basic calculus review, complex numbers and variables, Fourier
analysis, digital filters, spectral estimation, linear filtering in
the frequency domain, noise models, sampling, aliasing, the discrete
and fast Fourier transforms (DFT & FFT), Gibbs phenomenon, signal
quantization, and the impact of these concepts on estimation and
signal detection.

Advanced topics may include:

Wavelets, independent component analysis, image analysis (2D Fourier
analysis), multivariate time series, nonlinear processes.