Course content

Autoregressive and moving average based models for stationary and non-stationary time series. Parameter estimation. Model identification. Forecasting. ARCH and GARCH models for volatility. State space models (linear dynamic models) and the Kalman filter.

Learning outcome

1. Knowledge. The student knows the theoretical basis for modelling and analysis of time series data from engineering and finance. This includes knowledge about autoregressive and moving average models for stationary and non-stationary time series, and to know how to do model identification, parameter estimation and forecasting in such models. It also includes knowledge about ARCH and GARCH models for volatility, state space models (linear dynamic models) and the Kalman filter. 2. Skills. The student is able to use his or her knowledge about various time series models to fit models to observed time series data from engineering and finance, and to make forecasts based on the same data.

Learning methods and activities

Lectures and compulsory exercises.

Students are free to choose Norwegian or English for written assessments.

Compulsory assignments

• Exercises

Recommended previous knowledge

TMA4240/4245 Statistics or equivalent. The course demands some degree of maturity in Statistics and we also recommend to have TMA4267 Linear Statistical Models or TMA4255 Applied Statistics. TMA4145 Linear Methods (Vector- and Hilbert space) is also recommended.

Course materials

Will be announced at the start of the course.

Credit reductions