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Module ST3007: Applied Forecasting/Multivariate Data Analysis

Credit weighting (ECTS)
10 credits
Semester/term taught
Michaelmas term 2012 - Applied Forecasting
Hilary term 2013 - Multivariate Analysis
Contact Hours
22 weeks, 3 lectures including tutorials per week
Lecturers
Prof. Rozenn Dahyot (Statistics), Prof. Brett Houlding (Statistics)
Learning Outcomes
Applied Forecasting:
On successful completion of this module, students should be able to:
  • Define and describe the different patterns that can be found in times series and propose the methods that can be used for their analysis;
  • Program, analyse and select the best model for forecasting;
  • Interpret output of data analysis performed by a computer statistics package;
  Multivariate Analysis:
On successful completion of this module, students should be able to:
  • Define and describe various classical dimension reduction techniques for multivariate data;
  • Implement clustering and/or classification algorithms and assess and compare the results;
  • Interpret output of data analysis performed by a computer statistics package.
Module Content
Applied Forecasting:
  • Holt-Winters Algorithms for forecasting
  • ARIMA models for time series modelling;
  • Forecast and uncertainty using confidence intervals.
Multivariate Analysis:
  • Principal Components Analysis;
  • Multidimensional Scaling;
  • Factor Analysis;
  • Hierarchical and Iterative Clustering;
  • K-Nearest Neighbours;
  • Discriminant Analysis;
  • Logistic Regression
Module Prerequisite
Recommended Reading
Applied Forecasting:
  • Chatfield, C. (2004) The Analysis of Time Series, 6th edition, Chapman and Hall;
  • Makridakis, A., S.C. Wheelwright and R.J. Hyndman (1998) Forecasting: Methods and Applications, 3rd edition;
  Multivariate Analysis:
  • Introductin to Multivariate Analysis, C. Chatfield and A. Collins, Chapman & Hall

Assessment Detail

This module will be examined in a 3 hour examination in Trinity term. Continuous assessment will contribute 30% to the final grade for the module at the annual examination session.