Code: | M4113 | Acronym: | M4113 | Level: | 400 |
Keywords | |
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Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Computer Science |
Course/CS Responsible: | Master in Data Science |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
M:DS | 28 | Official Study Plan since 2018_M:DS | 1 | - | 6 | 42 | 162 |
M:ECAD | 9 | Study plan since 2021/2022 | 2 | - | 6 | 42 | 162 |
M:ENM | 17 | Official Study Plan since 2013-2014 | 1 | - | 6 | 42 | 162 |
2 |
By the end of the course, the student should be able to:
1.define basic time series concepts and terminology
2. select time series methods appropriate to forecast
3. use apropriate software
4. concisely summarize results of a time series analysis
Introduction. Time series data and their characteristics. Measures of dependence: autocorrelation and cross-correlation. Stationary time series. Estimation of correlation. Use of R for time series analysis.
Exploratory data analysis. Estimation of trend, cycle and seasonal components. Loess, STL and “Bureau of the Census” decompositions.
Time series models. ARMA models. Estimation and forecasting. Integrated ARIMA models for nonstationary data. Multiplicative Seasonal ARIMA models. Forecasting.
Box-Jenkins methodology: building SARIMA models- identification, estimation and diagnostic. Model selection. Unit root tests.
Forecasting: SARIMA models and exponential smoothing methods.
Visualizing and forecasting big time series data. Representation of many time series. Summarization of main characteristics. Automatic model selection. Automatic forecasting.
designation | Weight (%) |
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Teste | 50,00 |
Trabalho prático ou de projeto | 50,00 |
Total: | 100,00 |
designation | Time (hours) |
---|---|
Estudo autónomo | 80,00 |
Frequência das aulas | 42,00 |
Trabalho escrito | 40,00 |
Total: | 162,00 |