General description

A time series typically consists of a set of observations taken at equally spaced intervals over time. These kinds of series exist in many fields of application from economics to technology. The time dependence of the observations in most cases causes mutual dependences. The consideration of this dependence is what gives time series analysis the characteristic which separates it from other areas of statistics.

Learning outcomes

The student becomes familiar with the basic concepts of stochastic processes and with linear time series models. He learns to investigate time series graphically, to identify suitable models, to estimate them, and to make forecasts.

Contents

Characterizing time series graphically and quantitatively. Basic concepts and simple examples of stochastic processes. Time series regression. Linear modeling (ARIMA). Modeling variance (GARCH).

Teaching language

Finnish

Evaluation

Numeric 1-5.

Recommended year of study

2. year spring
3. year autumn
3. year spring

Study materials

1. Brockwell, P. J., Davis, R. A., Introduction to time series and forecasting. Springer 2003.
2. Chatfield, C., The Analysis of time series: an introduction. Chapman & Hall/CRC 2004.
3. Shumway, R., Stoffer, D., Time series analysis and its applications: with R examples. Springer 2006.

Belongs to following study modules