Time Series
- Details
- Category: Discipline
- Morettin, P. & Toloi, C. (2006). Análise de séries temporais. (2ª ed.). São Paulo: Edgard Blucher. Murteira, J.B., Muller, D.A. & Turkman, K.F. (1993). Análise de sucessões cronológicas. Lisboa: McGraw-Hill.
- Data Science
- 6321
- 23093
- Time Series
- ISMAT6321-23093
- 3
- 6
- 0
- 12
- Não
- Português
- The teaching methodology includes the expository method (TM1) to present the necessary contents, the demonstrative (TM2) to illustrate its application to practical examples and the active (TM3) for solving exercises with R and searching for solutions to proposed challenges (challenge based learning). Real datasets and statistical software are used to develop forecasting models and interpret temporal data behaviour.
- Mandatory
- At the end of the curricular unit, students should have the theoretical and computational knowledge necessary for modeling time series, using R: A Language and Environment for Statistical Computing.
- S1: Introduction to Time Series; S2: Notation and nomenclature; S3: Models for time series; S4: Trend and seasonality; S5: ARIMA models; S6: Identification, estimation, diagnosis and prediction with ARIMA models; S7: SARIMA models; S8: Use of computational packages: environment and R language.
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui:
- 1 teste de avaliação individual (40%);
- 1 seminário em grupo de 3 a 4 alunos (20%);
- 1 trabalho em grupo de 3 a 4 alunos (40%).
- Semestral
- The curricular unit Time Series belongs to the scientific area of Statistics and addresses methods for analysing and modelling time-ordered data. It develops skills in identifying temporal patterns, building forecasting models and interpreting results, which are fundamental for data-driven decision making.
