Time Series Analysis Laboratory

Recommended Prerequisites

Linear algebra, probability theory and statistics.

Teaching Methods

The teaching/learning process follows a learning approach of collaborative type, based on the presentation, analysis and discussion of concepts and techniques.

Theoretical classes: Presentation of the concepts, principles and fundamental techniques for time series analysis and forecasting. Examples of concrete situations will be presented to illustrate the practical interest of the techniques and their application to real conditions.

Practical classes: Practical problems addressing the theoretical concepts, their analysis, discussion and implementation.

Learning Outcomes

This course introduces fundamental concepts related to the theory, the design and the implementation of time series analysis and prediction in various domains. The course covers linear and nonlinear methods for time series analysis, capable to build forecasting models, in order to make inferences with application in prognosis and decision support systems.

This course will enable students to understand, identify, select and implement distinct time series and prediction methods and to apply these to practical situations in distinct domains.

The course will contribute to the acquisition of the following competences: 1. Instrumental: analysis and synthesis of complex problems. problem solving, namely in the forecasting area; 2. Personal: team work, critical reasoning; 3. Systematic: self-learning, research.

Work Placement(s)

No

Syllabus

1.Basic concepts about time series and prediction

2.Stocastic Processes

3.Basic description/characterization techniques

•          Decomposition models - Trend & seazonality estimation

•          Stationarity assessment & transformation

•          Autocorrelation

4. Linear models for stationary data:

•          Moving Average (MA)

•          AutoRegressive (AR)

•          AutoRegressive Moving Average (ARMA)

5.Linear models for non-stationary data:

•          AutoRegressive Integrated Moving Average (ARIMA)

•          Seasonal AutoRegressive Integrated Moving Average (SARIMA)

6.Forecasting:

•          Exponential smoothing

•          Box&Jenkins

7.Classical multivariate models

8.Non-linear modeling & forecasting:

•          Classical approaches

•          Modern approaches – Machine Learning

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

Additional References:

•          Linear Models and Time-Series Analysis Regression, ANOVA, ARMA and GARCH; Marc S. Paolella; 2019 JohnWiley & Sons Ltd; ISBN 9781119431985 (ePub) | ISBN 9781119431909

•          Time-Series Prediction and Applications - A Machine Intelligence Approach; Amit Konar, Diptendu Bhattacharya; 2017 Springer; ISBN 978-3-319-54596-7

•          Elements of Nonlinear Time Series Analysis and Forecasting; J. De Gooijer; 2017 Springer Series in Statistics; ISBN 978-3-319-43251-9

•          Nonlinear time Series analysis; Ruey S. Tsay, Rong Chen; 2019 John Wiley & Sons, Inc.; ISBN 978

Main References:

•          Time Series Analysis and Prediction - Class Notes; Cesar Teixeira and Marco Simões; 2021

•          The Analysis of Time Series: An Introduction with R; Chris Chatfield, Haipeng Xing; 7th edition; 2019 Chapman & Hall/CRC; ISBN 9781498795630

•          Probability and Random Processes With Applications to Signal Processing and Communications; Scott L. Miller, Donald Childers; 2nd edition; 2012 Elsevier; ISBN 9780123869814