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Module Descriptor School of Computer Science and Statistics

Module CodeST3010
Module NameApplied Forecasting
Module Short TitleN/a
ECTS5
Semester Taught Michaelmas term
Contact HoursLecture hours: 33Total hours: 33
Module PersonnelLecturing staff: Prof Rozenn Dahyot
Learning Outcomes

When students have successfully completed this module they should be able to:

  • Define and describe the different patterns that can be found in times series and propose algorithms and statistical models that are suitable for their analysis.
  • Program, analyse and select the best model for forecasting.
  • Interpret output of data analysis performed by a computer statistics package.
  • Compute predictions with their confidence intervals using the selected model.
Learning Aims

The aim of this module is to learn several mathematical techniques to analyse past observations for the purpose of predicting future outcomes and their associated uncertainty. The module will be practical, and will involve every student in extensive analysis of case study materials for a variety of time series data.

Module Content

Introduction to forecasting; ARIMA models, GARCH models, Kalman Filters,data transformations, seasonality, exponential smoothing and Holt Winters algorithms, performance measures. Use of transformations and differences.

Recommended Reading List

Forecasting - Methods and Applications, S. Makridakis, S. C. Wheelwright and R. J. Hyndman, Wiley Forecasting: principles and practice, https://www.otexts.org/fpp/

Module PrerequisitesBasic Statistics and Mathematics
Assessment Details

Exam: 100%

Module Website
Academic Year of DataN/a