Checking date: 07/06/2024

Course: 2024/2025

Time Series Analysis
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)


Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Probability Statistical Inference Programming in R
The student will acquire the following knowledge: 1) Proficiency of descriptive techniques of univariate time series 2) Modeling of univariate time series using moving averages, exponential smoothing and periodogram methods 3) Modeling of univariate time series using machine learning methods. 4) Stationary time series modeling. AR models. MA models. ARIMA models. 5) Proficiency of model identification techniques. Diagnostics. 6) Modeling of multivariate time series. VAR models. Identification of models. Diagnostics. 7) Proficiency of model identification techniques. Diagnostics.
Skills and learning outcomes
Description of contents: programme
1. Introduction. 2. Time series decomposition. 3. ARIMA models. 4. Dynamic regression and Machine Learning. 5. Multivariate time-series. 6. Volatility models.
Learning activities and methodology
The classes consist of a mixture of presentations on the fundamental concepts of the subject and the presentation of practical cases through the use of software. The languages R and Python are preferably used. Students are expected to bring their own laptops to experiment with the code during the lectures. * Training activities   - AF1: Theoretical lesson.   - AF2: Practical lesson.   - AF5: Tutorials.   - AF6: Group work.   - AF7: Individual work.   - AF8: On-site evaluation tests. * Teaching methodologies   - MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning.   - MD2: Critical reading of texts recommended by the professor of the subject: press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject.   - MD3: Resolution of practical cases, problems, etc. posed by the teacher individually or in groups.   - MD4: Presentation and discussion in class, under the moderation of the professor of topics related to the content of the subject, as well as case studies.   - MD5: Preparation of papers and reports individually or in groups.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Daniel Peña. Análisis de series temporales. Alianza Editorial. 2005
  • Lazzeri, F. Machine Learning for Time Series Forecasting with Python. Wiley. 2020
  • Rob J Hyndman and George Athanasopoulos. Forecasting: Principles and Practice. OTexts: Melbourne, Australia. 2021
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.

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