|ECTS Weighting||5 ECTS|
|Semester taught||Semester 1|
|Module Coordinator/s||Alessio Benavoli|
Module Learning Outcomes
On successful completion of this module, students will be able to:
LO1: 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.
LO2: Program, analyse and select the best model for forecasting.
LO3: Interpret output of data analysis performed by a computer statistics package.
LO4: Compute predictions with their confidence intervals using the selected model.
Introduction to Forecasting; ARIMA models, data transformations, seasonality, exponential smoothing and Holt Winters algorithms, performance measures. Use of transformations and differences.
Teaching and learning Methods
Lectures, online quizzes, online Discussion Board
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of total||Week set||Week Due|
Analysis of time series
|Examination||Real-time exam (2hrs)||LO1-4||60%|
Examination, 2 hours, 100%
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||0 hours|
|Tutorial or seminar||0 hours|
|Independent study (outside scheduled contact hours), broken down by:||83 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable||40 hours|
|completion of assessments (including examination, if applicable)||43 hours|
|Total Hours||116 hours|
Recommended Reading List
Forecasting: principles and practice by R. Hyndman and G. Athanasopoulos online book at http://otexts.com/fpp/
Other/alternative non-module prerequisites:
statistics (linear regression), R programming languages.