Time Series Predictions

Lecturer:

Emil Pelikán (gar.)

Tutor:

Emil Pelikán (gar.)

Supervisor:

Department of Control and Telematics

Synopsis:

Knowledge of forecasting models and qualitative forecasting evaluation.

Requirements:

Basic knowledge of mathematical statistics.

Syllabus of lectures:

Quantitative and qualitative forecasting, causal models, time-series methods, least squares estimates. Useful descriptive statistics, accuracy of forecasting methods, MAE, MAPE, RMSE, entropy measures, naive predictions (persistency). Linear forecasting models, covariance and correlation coefficients, smoothing methods, regression methods, Box-Jenkins time-series methods, AR, MA, ARMA,ARIMA models.

Syllabus of tutorials:

Study Objective:

Study materials:

Makridakis S., Wheelwright S. C., Hyndman R. J.: Forecasting: Methods and Applications, Third edition, John Willey & Sons, 1998.

Anděl J.: Statistická analýza časových řad, Praha, SNTL, 1976.

Box G. E. P., Jenkins G. M.: Time Series Analysis: Forecasting and Control, Holden-Day, San Francisko, Revised Edition, 1976.

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: