Faculty of Information Technology and Communication Sciences
Language of instruction
Technology and Natural Sciences
Mode of study
Time Series Analysis, 5 cr
Language of instruction: English
After the course, students will be able to characterize the basic properties of a time series and decompose it into a trend, seasonal component and noise. He/she will also be able to identify and diagnose linear time series models, estimate their parameters and use them in forecasting. Further, he will be able to analyze practical time series data using R.
Modeling trends and seasonality, Autocovariance function and partial autocorrelation function, stationary time series models (ARMA), nonstationary (ARIMA), and seasonal time series models (SARIMA), model estimation and forecasting in R