Research subject Time series analysis
When measuring a variable periodically in time, the observations form a time series. Unlike many other areas in statistics, time series analysis is no more than around 100 years old.
Time series analysis is based on the type of data where a variable is regularly measured in time. The method is primarily used to decompose time series. For example, seasonal adjustment, identify and model the systematic variation, identify and model the time-based dependencies and forecasts.
Nowadays, the so-called Box-Jenkins models are perhaps the most commonly used and many techniques used for forecasting and seasonal adjustment can be traced back to these models.
Another line of development are non-linear generalizations, mainly ARCH (AutoRegressive Conditional Heteroscedasticity) - and GARCH- (G = Generalized) models which have proved very useful, especially for financial time series. The invention of them and the release of a way to correct the models for errors, provided C. W. J. Granger and R. F. Engle with the Nobel Memorial Prize in Economic Sciences in 2003.
Related research subject
Statistics
On this page
Researchers
Andriy Andreev
Universitetslektor

Oskar Gustafsson
Universitetslektor

Pär Gunnar Victor Stockhammar
Universitetslektor

Mattias Villani
Professor
