Time Series Analysis and Forecasting

The classes are, in accordance with the curriculum, theoretical-practical, planned and prepared to actively involve students at various
moments or throughout the entire class.
In the theoretical part, which introduces concepts, fundamental results and methods, the expository method will tend to be used,
interspersed with tasks that encourage more active participation from all students. These tasks include asking questions to and by students,
either orally and/or on a platform, and also proposing debate/discussion in small groups on some aspect/topic presented.
The practical part will be devoted to the full development of the listed skills, through the commented exemplification of procedures and/or
problem solving under the guidance/tutoring of the teacher, encouraging independent work or work in small groups, evolving into projectbased
learning, with the completion of the assignment. There will be a strong interaction between theory and practice, giving, whenever
possible, a central role to the visualisation and treatment of concrete and real situations.
Good monitoring of classes by students requires regular attendance and a willingness to remain involved beyond the classroom, with the
start or completion of tasks agreed in class.

Data description and forecasting tasks often involve data in which the temporal component cannot be neglected. Therefore, in this curricular
unit, the focus is on time series data. It is intended that the student is able to:
O1) Explain the key concepts of time series analysis and forecasting.
O2) Conduct exploratory analysis and the respective processing of time series data, taking into account the defined analysis and/or
forecasting objectives.
O3) Select, apply and compare suitable forecasting methods for time series, critically evaluating and following best practices in assessing
the predictive performance of the models obtained.
O4) Design, implement and communicate a complete forecasting solution for time series, justifying the methodological decisions taken.

1. Time series analysis
1.1. Introduction: definition, components, autocorrelation, stationarity, and visualisations
1.2. Decomposition: models, methods (classical and STL), and applications
1.3. Preparation: time aggregation, adjustments, transformations, variable extraction, missing values, and outliers
2. Time series forecasting
2.1. Introduction: basic concepts, notation, types of forecasts, statistical forecasting
2.2. Statistical methods: elementary, exponential smoothing and ETS, ARIMA and SARIMA
2.3. Evaluation of the predictive performance: procedures and measures
2.4. Extensions: automatic model selection, and forecasting with exogenous variables

NAO