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.
-
- 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.