Time Series

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.

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.

Lectures and compulsory exercises.

The course is taught every second year. The course will be given in autumn in years of odd numbers.

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