Overview
Academic contacts
Offerings
Requisites
Enrolment rules
Other learning activities
Learning activities
Learning outcomes
Recognize and understand the nature of correlated data, and the computational methods for their analysis.
Recognize autocorrelated data or data which are correlated over time (as is in the nature of many time series data) and learn techniques - of both a simple nature and of a more complex nature, by way of which one may analyse such data. These techniques can include straight forward regression in time or further more appropriate methods in some contexts which involve what is known as the “Time Domain Approach”.
Recognize cyclical patterns that are analysed in the frequency domain. Ideas of spectral analysis and the “Frequency Domain Approach” are countenanced, and numerical approaches.
Understand bivariate processes that can be analysed using the Cross Spectrum and these lead into the theory of Linear Systems in both the Time Domain and the Frequency Domain.