SDSC6012 - Time Series and Recurrent Neural Networks

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
Course Offering Term*:
Semester B 2021/22

* The offering term is subject to change without prior notice
Course Aims

In macroeconomics and other areas of business, science, and engineering, a lot of data is available as time series data sets. In this course, students will study the statistical tools that are used to analyse such data and apply them to real world data with the help of the statistical software R. The first part of the course starts by reviewing basic stochastic process and time series concepts. Then, ARMA models are introduced together with estimation methods for the models and properties of their forecasts. The GARCH model for modelling variation in error variances is also taught. The second part of the course introduces recurrent neural networks for time series forecast. Throughout the course, emphasis will be given to analysis of data using the taught methods with R software. 

Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 50%
Examination: 50%
Examination Duration: 2 hours
Detailed Course Information


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School of Data Science