Forecasting Methods
Objectives
- To obtain an understanding of common statistical methods used in business and economic forecasting;
- To develop the computer skills required to forecast business and economic time series data;
- To gain insights into the problems of implementing and operating large scale forecasting systems for use in business.
General characterization
Code
100086
Credits
6.0
Responsible teacher
Carolina Maria de Abreu Braziel Shaul
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
Matrix algebra and statistics (recommend)
Bibliography
Shumway, R.H. and Stoffer, D.S. Time Series Analysis and its Application with R Examples, 3rd edition, Springer, 2011. (http://www.stat.pitt.edu/stoffer/tsa3/); Makridakis, S., Wheelwright, S.C., Hyndman, R.J. Forecasting: Methods and Applications, 3rd edition, John Wiley & Sons, 1998.; Forecasting: principles and practice: https://www.otexts.org/book/fpp; Little Book of R for Time Series: http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/; Murteira, B., Muller, D., Turkman F. Análise de Sucessões Cronológicas, 1ª edição, McGraw Hill, 1993
Teaching method
Theoretical and pratical classes. On the theoretical class students will learn the main concepts of forecasting methods. On practical classes students will solve exercises and practical cases on the concepts addressed in the theoretical class.
Evaluation method
Two options:
- Continuous assessment (Three multiple choice mid-term tests (50%) and One assignment (50%))
- Second exam period (100%)
There is no first exam period!
Subject matter
- Introduction to forecasting and R
- Time series graphics
- Time series decomposition
- The forecaster's toolbox
- Time Series Regression
- Exponential smoothing
- ARIMA models
- Dynamic regression
- Advanced forecasting methods
Programs
Programs where the course is taught: