Workshop 3: Multivariate Time Series Analysis with R CANCELLED
Target group: Advanced Learners
Available seats: 15
This course is designed to provide PhD students with an in-depth understanding of multivariate time series analysis using the R programming language. The course will cover various techniques and models for analyzing and forecasting time series data, including GARCH and ARIMA models.
The course will start with an introduction to time series analysis and its importance in the real world. Students will learn about the different types of time series data and their characteristics, as well as various methods for visualizing and exploring time series data.
The course will then cover various modelling techniques for multivariate time series analysis.
The course will also cover ARIMA models, which are widely used for time series forecasting. Students will learn how to use the arima function in R to fit and forecast ARIMA models for (multivariate) time series data.
Finally, the course will cover rugarch, which is a package in R for modelling and forecasting univariate and multivariate GARCH models. Students will learn how to use rugarch to estimate and forecast GARCH models for multivariate time series data.
Throughout the course, students will work on practical exercises and projects to reinforce their learning and develop their skills in R programming and multivariate time series analysis. By the end of the course, students will have a solid understanding of the techniques and models used in multivariate time series analysis, as well as practical experience using R for time series analysis and forecasting.
To receive a certificate of active participation you need to hand in a written analysis of a time series (ideally related to your research topic) covering the methods discussed in the workshop and discussing the usability of the methods for your particular PhD project.
Lecturer: Dr. Markus Fülle