Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
12 |
4 |
8 |
4 weeks |
120 hours |
Instructor
Edwin Moises Marcos Ortega
Fábio Prataviera
Vitor Augusto Ozaki
Objective
Provide students with knowledge of time series models, including estimation and diagnostics.
Content
1. Preliminary Concepts. 2. Smoothing models. 3. ARIMA models. 4.
Identification, Maximum Likelihood Estimation, Diagnosis and Forecasting.
5. Aspects of Bayesian estimation in time series 6. Seasonal models.
7. Spectral analysis and filtering.
All methods and applications will be carried out with the support of software
Time Series data analysis (R, Python and/or SAS).
Bibliography
1- Brockwell, P. J. and Davis, R. A. (2009). Time Series: Theory and Methods (2nd Edition). New York:
Springer.2- Broemeling, L. D. (2019). Bayesian analysis of time series. CRC Press.
3- Box, G. E. P., Jenkins, G.M., Reinsel, G.C. and Ljung, GM. (2015). Time Series Analysis: Forecasting
and Control, 5th Edition. Wiley.
4- Cowpertwait, P. S. P e Metcalfe, A. V. (2009). Introductory Time Series with R. Springer.
5- Morettin, P. A. e Toloi, M. C. (2018). Análise de Séries Temporais: Modelos lineares univariados. 3ªEdição. Editora Edgard Blucher.
6- Morettin, P. A. e Toloi, M. C. (2020). Análise de Séries Temporais: Modelos multivariados e não lineares. Editora Edgard Blucher.
7- Lutkepohl, H. Introduction to multiple time series analysis. Springer Science & Business Media, 2013.
8- Shumway, R. H. y D. S. Stoffer (2010). Time Series Analysis and Its Applications (Third Edition). New York: Springer.
9- Tsay, R. S. (2010). Analysis of Financial Time Series (3rd Edition). Hoboken: Wiley.