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# Time Series and Forecasting

 Code: M4113 Acronym: M4113 Level: 400

Keywords
Classification Keyword
OFICIAL Mathematics

## Instance: 2020/2021 - 1S

 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

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.

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

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.

NA

NA

NA

### Classification improvement

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

NA