Prof Leonard Smith PEL.4.01C

This course is available on the MSc in Applicable Mathematics, MSc in Econometrics and Mathematical Economics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

It is recommended that students have completed Time Series (ST422).

An introduction to the analysis of actual time series observations of real-world processes. The course casts both modern nonlinear methods and more traditional linear methods in a geometric approach. It introduces the properties of nonlinear mathematical models, covers chaos and the dynamics of uncertainty, and demonstrates the fundamental limitations in applied analysis which arise from model inadequacy. Fundamental aspects of  predictability are addressed. Decision support under uncertainty is considered, with examples of economic impacts of forecasting, including weather and climate. The student will leave with a toolkit for the analysis and modelling of real data, with insights into how to evaluate which methods to employ (linear/non-linear, deterministic/stochastic) in a given problem, how to interpret the results in context, and how to avoid over interpreting nice theorems in practical circumstances. Concrete applications in economics (price time series, electricity demand, energy futures) and environment (weather, climate) as well as analytically tractable illustration from mathematics are considered.

20 hours of lectures and 10 hours of computer workshops in the WT. 1 hour of lectures in the ST.

Week 6 will be used as a reading week.

K Beven, Environmental Modelling: An uncertain Future? Routledge (2009); H Kantz & T Schreiber, Non-linear Time Series Analysis; E Ott, T Sauer & J A Yorke (Eds), Coping with Chaos: Analysis of Chaotic Data and The Exploitation of Chaotic Systems; E Ott, Chaos in Dynamical Systems; R Tsay, Analysis of Financial Time Series; L.A. Smith, Chaos: A Very Short Introduction. Oxford University Press (2007)

Exam (70%, duration: 2 hours) in the spring exam period.
Project (30%) in the ST.