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Time Series and Forecasting

Code: M4113     Acronym: M4113     Level: 400

Keywords
Classification Keyword
OFICIAL Mathematics

Instance: 2020/2021 - 1S Ícone do Moodle

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Data Science

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
M:DS 20 Official Study Plan since 2018_M:DS 1 - 6 42 162
M:ENM 11 Official Study Plan since 2013-2014 1 - 6 42 162
2

Teaching Staff - Responsibilities

Teacher Responsibility
Maria Eduarda da Rocha Pinto Augusto da Silva

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
Maria Eduarda da Rocha Pinto Augusto da Silva 3,00

Teaching language

Suitable for English-speaking students

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

R

Evaluation Type

Distributed evaluation with 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

Mid term test 25% + Final test 2 50% + Project 50%. 

Minimum grade of 7/20 for each of the components.

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
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