This module consists of a weekly seminar discussion in Term 2. The aim of this module is to introduce some of the core concepts for STEaPP-based research, as...

This module consists of a weekly seminar discussion in Term 2. The aim of this module is to introduce some of the core concepts for STEaPP-based research, as well as provide opportunity to practice individual and group-based critical review of literature. Ten specified books that relate to STEaPP research interests will be discussed. By the end of this module, each DTP candidates should facilitate at least one seminar for critical book discussion, as well as produce a written summary of critical discussion points. At the doctoral progression, candidates are expected to be able to relate their research to the books studied.

This module consists of a weekly seminar discussion in Term 1. The aim of this module is to introduce some of the core concepts for STEaPP-based research, as...

This module consists of a weekly seminar discussion in Term 1. The aim of this module is to introduce some of the core concepts for STEaPP-based research, as well as provide opportunity to practice individual and group-based critical review of literature. Ten specified books that relate to STEaPP research interests will be discussed. By the end of this module, each DTP candidates should facilitate at least one seminar for critical book discussion, as well as produce a written summary of critical discussion points. At the doctoral progression, candidates are expected to be able to relate their research to the books studied.

Modernism|counter-modernism|W. B. Yeats|T. S. Eliot|James Joyce|Virginia Woolf|

In 1900, various European countries – Britain, France, Germany, Austria – were competing with each other for imperial supremacy. Over the next five...

Aims - This module aims to introduce methods of finding and extrapolating patterns in time-ordered sequences. It is primarily intended for third and fourth...

Aims - This module aims to introduce methods of finding and extrapolating patterns in time-ordered sequences. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programmes offered by the Department of Statistical Science, or jointly with other departments. It also serves as an optional module for students taking the mathematics and statistics stream of the Natural Sciences degree (with prerequisite: STAT0007).

Objectives - On successful completion of the module, a student should be familiar with the most commonly-used models for time series; be able to derive properties of time series models; be able to select, fit, check and use appropriate models for time-ordered data sequences; understand and be able to interpret the output from the time series module of a variety of standard software packages.

Applications - Time series data take the form of observations of one or more processes over time, where the structure of the temporal dependence between observations is the object of interest. Such data arise in many application areas including economics, engineering and the natural and social sciences. The use of historical information to estimate characteristics of observed processes, and to construct forecasts together with assessments of the associated uncertainty, is widespread in these application areas.

Content - Forecasting as the discovery and extrapolation of patterns in time ordered data. Revision of descriptive measures for multivariate distributions. Descriptive techniques for time series. Models for stationary processes: derivation of properties. Box-Jenkins approach to forecasting: model identification, estimation, verification. Forecasting using ARIMA and structural models. Forecast assessment. State space models and Kalman Filter. Comparison of procedures. Practical aspects of forecasting. Case studies in forecasting.

Key texts - Available from ReadingLists@UCL.