Forecasting Methods


1. To understand the characteristics of time series data
2. To understand moving average models and partial autocorrelation as foundations for analysis of time series data
3. To understand smoothing and how to remove trends when working with time series data
4. To understand ARMA and ARIMA time series models
5. To identify and interpret various patterns for intervention effects

General characterization





Responsible teacher

Jorge Morais Mendes


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




- Shumway, R.H. and Stoffer, D.S. (2011). Time Series Analysis and its Applications with Examples in R, 3rd edition, Springer.
- Hyndman, R. J., Athanasopoulos, G. (2018). FORECASTING: PRINCIPLES AND PRACTICE, 2nd edition

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.

Evaluation method

1st call: project (40%), first round exam (60%)
2nd call: final exam (100%)

Subject matter

1. Time Series Basics
2. AR Models, ACF
3. MA Models, PACF
4. ARMA & ARIMA models
5. Seasonal Models
6. Smoothing and Decomposition Methods
7. Intervention Analysis