Análisis y Predicción de Series Temporales, 2025.

2025-11-26 Session

• Trevor Hastie - Gradient Boosting Machine Learning

Transitioning from R to Python in forecasting:

• Hyndman’s Forecasting: Principles and Practice, the Pythonic Way
• Nixtlaverse
• Blog: Data Science with Marco (Peixeiro) including Time Series Forecasting with SARIMA in Python

2025-11-12 Session

• RNN_extra_slides.pdf

2025-11-05 Session

• Example of level shift

2025-10-29 Session

• Transfer functions for seasonal time series: Lab7_TF_Seasonal_v2
• Transfer functions with more than one input: Lab7_TF_two_inputs_example.R
• macro_data.csv

2025-10-22 Session

• Splits in Forecasting (via the Python sktime lbrary)

2025-10-08 Session

• Midterm 2023 statement available in Moodle
• Dynamic Regression models
• Recommended reading: Chapter 10 of Hyndman & Athanasopoulos (2021) and the corresponding videos. (Chapter 9 in hyndman2021fpp2?) covers the same ideas, but using previous versions of the R libraries, you may find it useful as well. Section 5.5 of Shumway & Stoffer (2017) deals with Transfer Function Modeling, but to read it you need the previous chapters material.

2025-10-01 Session

• Última semana para apuntarse al Acto de la Cátedra Endesa de Aplicaciones de la IA (21/10/2025)
• SARIMA, lecture notes
• Recommended reading: Sections 5.8 to 5.10 of Hyndman & Athanasopoulos (2021) and the corresponding videos. The code for those sections uses a more advanced R syntax than our lab code, but the results are essentially the same.

2025-09-24 Session

• ARIMA, lecture notes
• Recommended reading: Chapter 9 of fpp2 up to Section 9.8 (inclusive). Also watch the videos in that chapter.

2025-09-17 Session

• ARMA processes, lecture notes
• Recommended reading: Same as the previous lecture, plus Ch.11 of Krispin (2019). For more mathematical background up to Ch02 of Shumway & Stoffer (2017)

2025-09-10 Session

• Lab 2 ARMA models, additional notes
• Recommended reading: Section 2.9 and Chapters 3 and 6 of fpp2. Also watch the videos in the corresponding chapters of fpp3
• Examples of MA process
• Python code

2025-09-03 Session

• Lab. 1 Decomposition, additional notes
• Recommended reading: Chapters 1 and 2 of fpp2. Also watch the videos in the corresponding chapters of fpp3
• Forecasting: Principles and Practice, the Pythonic Way
• Time-Series Forecasting: Deep Learning vs Statistics. A Medium post (2023) by N. Kafritsas.
• Python code

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Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice, 3rd edition. OTexts. Retrieved from https://otexts.com/fpp3/

Krispin, R. (2019). Hands-on time series analysis with r: Perform time series analysis and forecasting using r. Packt Publishing Ltd. Retrieved from https://github.com/PacktPublishing/Hands-On-Time-Series-Analysis-with-R

Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With r examples. Springer International Publishing. Retrieved from https://link.springer.com/book/10.1007/978-3-319-52452-8