Forecasting Methods


The main objective of this course is to develop the skills needed to do empirical research in fields operating with time-series data sets.
The course intends to meet two goals: it provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models. Much of the statistical methodology is concerned with models in which the observations are assumed to be independent. However, many data sets occur in the form of time series where observations are dependent. In this course, we will concentrate on time series analysis, with a balance between theory and applications. 

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

  • (50%) Final exam (1st and 2nd rounds)
  • (50%) Project (optional)
  • Final grade: maximum (Final exam grade;0.5*(Final exam grade)+0.5*Project grade))

A minimum grade of 8.5 is required in the final exam to pass.

Subject matter

1. Time Series Basics
2. AR Models, ACF
3. MA Models, PACF
4. ARMA & ARIMA models
5. Seasonal Models