Time Series and Forecasting

Code:

M.IA034

Acronym:

STP

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	33	Syllabus	1	-	6	42	162
			2

Teaching language

English

Objectives

The aim of this course is to introduce the students to time series analysis methods.

Learning outcomes and competences

By the end of the course, the student should be able to:

1.define basic time series concepts and terminology;
2. select time series methods appropriate to forecast;
3. use apropriate software;
4. concisely summarize results of a time series analysis.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Probability and Statistics at introductory level.

Program

Introduction. Time series data and their characteristics. Measures of dependence: autocorrelation and cross-correlation. Stationary time series. Estimation of correlation. Use of R for time series analysis.

Exploratory data analysis. Estimation of trend, cycle and seasonal components. Loess, STL and “Bureau of the Census” decompositions.

Time series models. ARMA models. Estimation and forecasting. Integrated ARIMA models for nonstationary data. Multiplicative Seasonal ARIMA models. Forecasting.

Box-Jenkins methodology: building SARIMA models- identification, estimation and diagnostic. Model selection. Unit root tests.

Forecasting: SARIMA models and exponential smoothing methods.

Visualizing and forecasting big time series data. Representation of many time series. Summarization of main characteristics. Automatic model selection. Automatic forecasting.

Mandatory literature

Cryer, Jonathan D.; Time series analysis : with applications in R, Adison-Wesley, 2009. ISBN: 0-321-32216-9

Teaching methods and learning activities

Classes; example classes and laboratory classes.

Software

Python
R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Teste	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	80,00
Frequência das aulas	42,00
Trabalho escrito	40,00
Total:	162,00

Eligibility for exams

Not applicable.

Calculation formula of final grade

• Written component (CE) - 1 Test (20%)+ 1 Exam (80%)


•  Project (CP) - min report with exploratory data analysis (20%) + Report with presentation (80%). 

Both componentes require a minimum of 8/20

Final mark= 0.5 CE + 0.5 CP

Examinations or Special Assignments

NA

Internship work/project

NA

Special assessment (TE, DA, ...)

NA

Classification improvement

The student must take a written exam and resubmit a project.

Observations

NA