Module Descriptor School of Computer Science and Statistics
|Module Name||Applied Forecasting|
|Module Short Title||N/a|
|Semester Taught||Michaelmas term|
|Contact Hours||Lecture hours: 33Total hours: 33|
|Module Personnel||Lecturing staff: Prof Rozenn Dahyot|
When students have successfully completed this module they should be able to:
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.
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 Prerequisites||Basic Statistics and Mathematics|
|Academic Year of Data||N/a|