Prediction of time series

Course guarantor:

Šárka Voráčová

Lecturer:

Tutor:

Supervisor:

Department Děčín

Synopsis:

Introduction to time series prediction, meaning of prediction, basics of quantitative prediction. Methods for predictive quality evaluation, descriptive statistics, MAE, MAPE, RMSE, naive prediction, prediction for general formula of loss function. Calculation and programming environment R. Regression models, basics of linear regression, simple regression. Multiple regression, statistical tests of linear dependence, selection of input variables.

Requirements:

mathematical analysis, statistics

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

The aim of the course is to acquaint students with the possibilities of prediction of time series, to teach students to understand the prediction quality evaluation and to create simple regression models.

Study materials:

Durbin,J.-Koopman, S.-J. (2001), TimeSeriesAnalysisby StateSpaceMethods, Oxford University Press.

Harvey,A.C. (1989): Forecasting, structuraltimeseriesmodels and Kalman filter.

Pinheiro,J.C.-Bates,D.M. (2000): Mixed-effectsmodels in S and S-plus. Springer. New York.

Pekár,S.-Brabec,M. (2016): Modern Analysis of Biological Data, I. Generalized

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: