| Study program / study programs: Advanced data analytics |
| Course: Introduction to time series analysis |
| Teacher(s): Marija Mitrović Dankulov, Aleksandra Alorić, Andrej Korenić |
| Course status: Elective |
| ECTS points: 10 |
| Prerequisites: none |
| Course objective: Acquiring basic knowledge about theoretical and applied aspects of time series analysis. |
| Learning outcomes: Students will acquire knowledge about basic concepts of time series analysis and their application to data science. They will be able to analyze in detail continuous and discrete time series, to describe their characteristics, to choose a proper theoretical model and to infer the interaction matrix based on time series correlations. |
| Course structure and content: Main course topics: Stationary processes, auto-correlation and autocovariance functions; Moving Average (MA) processes, Auto-Regressive (AR) processes and Auto-Regressive/Moving Average (ARMA) processes; correlogram; spectral analysis, periodogram; elements of estimation and forecasting and applications to empirical data. Detrended fluctuation analysis. Hurst exponent. Fourier transform for time series analysis and prediction. Time series correlations and network extraction. |
| Literature/Readings: Shumway, Robert H., and David S. Stoffer (2011), Time series analysis and its applications: with R examples, Springer, ISBN: 978-1-4419-7864-6 |
| The number of class hours per week: Lectures: 4 Labs: 0 Workshops: 0 Research study: 3 Other classes: 0 |
| Teaching methods: Individual and group work; lectures and labs |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Homework): 40 Final exam (Project: real-world application): 60 |
