Course contents

Stochastic processes find applications in a wide variety of fields and offer a refined and powerful framework to examine and analyse time series. This course presents the basics for the treatment of stochastic signals and time series. For a stochastic process to be stationary, the mechanism of the generation of the data should not change with time. Mathematical tools for processing of such data is covariance and spectral analysis, where different models could be used. Some usual models are autoregressive (AR) and moving average (MA) processes. For appropriate modelling, we need reliable estimation of covariance functions and spectral densities. Linear filtering, differentiation and integration of stationary stochastic processes are other important parts of this course.