SDSC6012 - Time Series and Recurrent Neural Networks | ||||||||
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* 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. First, students will engage in reviewing basic stochastic process and time series concepts. Then, they will expand their knowledge on ARMA models together with estimation methods for the models and properties of their forecasts, as well as the GARCH model for modelling variation in error variances. Second, students will engage in recurrent neural networks for time series forecast. Throughout the course, students will focus on 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 | ||||||||
SDSC6012.pdf | ||||||||
Useful Links | ||||||||
Department of Data Science |