Time Series Prediction

Lecturer:

Tutor:

Supervisor:

Department of Control and Telematics

Synopsis:

Quantitative and qualitative forecasting, causal models, time-series methods, least squares\r\nestimates. 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. Input variable selections, statistical tests.

Requirements:

Basic knowledge of mathematical statistics.

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Knowledge of forecasting models and qualitative forecasting evaluation.

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:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: