Time Series Analysis
In this module, time series forecasting methods are introduced and explored. Students will gain a working knowledge of the nature and processes used in relation to time series data and confidently recognize and understand trends that exist within that data. This information will be used to make predictions or forecasts.
Students will analyse and forecast macroeconomic variables such as GDP and inflation. Additionally, students will work with complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing, and other models.
Core Reading List:
Hands on Time Series Analysis with R
Rami Krispin
Publisher: Packt
Copyright Year: May 2019
- Accreditation: ECTS Accredited (EQF7)
- Total workload: 150 hours
- Requires extra purchases (outside texts, etc.): Yes, purchases required
- ID verification: Required
- Admission requirements: Application required
- Minimum education requirement for students: Undergraduate
Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.
English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.