Statistical Analysis of Time Series - Seminar

Relations:

In order to register for the course NI-SCRS, the student must have registered for the course NI-SCR no later than in the same semester.

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

The seminar for the course Statistical Analysis of Time Series (NI-SCR) extends foundational knowledge and provides an overview of modern methods, particularly in machine learning and artificial intelligence. Instead of detailed theory exposition, the emphasis is put on understanding the underlying principles and their practical applications. The first part of the semester focuses on extensions of classical methods, especially the treatment of seasonality and multiple seasonality with spectral interpretation. The second part covers fundamental tasks in time series analysis using machine learning methods, some of which are introduced in the courses BI-ML1 and BI-ML2 for classical (non-time-series) problems. The final part is devoted to modern artificial intelligence methods. The course is oriented toward the practical use of open-source tools, particularly PyTorch, sktime, scikit-learn, tslearn, and tsfresh.

Requirements:

Syllabus of lectures:

1. Spectral analysis of time series.

2. Multiple seasonality in time series.

3. Metrics and other similarity measures in time series analysis (DTW).

4. Machine learning methods I: Random forests.

5. Machine learning methods II: XGBoost.

6. Machine learning methods III: LightGBM.

7. Machine learning methods IV: k-NN.

8. Classification in time series analysis: Shapelets.

9. Basics of time series clustering.

10. Advanced AI methods in time series analysis I (transformers).

11. Advanced AI methods in time series analysis II (Chronos).

12. Advanced AI methods in time series analysis III (overview).

13. Course summary.

Syllabus of tutorials:

Spectral analysis of time series.

Multiple seasonality in time series.

Metrics and other similarity measures in time series analysis (DTW).

Machine learning methods I: Random forests.

Machine learning methods II: XGBoost.

Machine learning methods III: LightGBM.

Machine learning methods IV: k-NN.

Classification in time series analysis: Shapelets.

Basics of time series clustering.

Advanced AI methods in time series analysis I (transformers).

Advanced AI methods in time series analysis II (Chronos).

Advanced AI methods in time series analysis III (overview).

Course summary.

Study Objective:

Study materials:

Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2.

Auffarth, B. (2021) Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods. Packt Publ., 2021.

A. F. Ansari et al., Chronos: Learning the Language of Time Series, Nov. 04, 2024, arXiv:2403.07815. doi: 10.48550/arXiv.2403.07815.

Kong, X., Chen, Z., Liu, W. et al. Deep learning for time series forecasting: a survey. Int. J. Mach. Learn. & Cyber. 16, 50795112 (2025). https://doi.org/10.1007/s13042-025-02560-w.

M. Vlachos, D. Gunopoulos, and G. Kollios. 2002. Discovering Similar Multidimensional

Young-Seon Jeong, Myong K. Jeong, Olufemi A. Omitaomu, Weighted dynamic time warping for time series classification, Pattern Recognition, Volume 44, Issue 9, 2011, Pages 2231-2240, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2010.09.022.

J. Grabocka et al. Learning Time-Series Shapelets. SIGKDD 2014.

Note:

Course materials are available at https://courses.fit.cvut.cz/NI-SCR/ The course is presented in Czech.

Further information:

https://courses.fit.cvut.cz/NI-SCR/

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master specialization Computer Security, in Czech, 2020 (elective course)
• Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 2020 (elective course)
• Master specialization Management Informatics, in Czech, 2020 (elective course)
• Master specialization Software Engineering, in Czech, 2020 (elective course)
• Master specialization System Programming, in Czech, version from 2020 (elective course)
• Master specialization Web Engineering, in Czech, 2020 (elective course)
• Master specialization Knowledge Engineering, in Czech, 2020 (elective course)
• Master specialization Computer Science, in Czech, 2020 (elective course)
• Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (elective course)
• Study plan for Ukrainian refugees (elective course)
• Master specialization System Programming, in Czech, version from 2023 (elective course)
• Master specialization Computer Science, in Czech, 2023 (elective course)
• Quantum Informatics (elective course)
• Master specialization Computer Security, in Czech, 2026 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 2026 (elective course)
• Master specialization Computer Science, in Czech, 2026 (elective course)
• Master specialization Programming Languages, in Czech, 2026 (elective course)
• Master specialization Artificial Intelligence, in Czech, 2026 (elective course)
• Master programme, for the phase of study without specialisation, ver. for 2026 and higher (elective course)