Forecasting Methods and Time Series

Code:

2MDA08

Acronym:

MPST

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2022/2023 - 2S

Active?	Yes
Course/CS Responsible:	Master in Modeling, Data Analysis and Decision Support Systems

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MADSAD	46	Official Syllabus - after 2020-2021	1	-	6	42	162
ME	3	Official Syllabus after 2021-2022	1	-	6	42	162

Teaching language

English

Objectives

The aim of this course is to introduce the students to time series analysis methods with a special emphasis on forecasting.

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 for forecasting
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)

Introductory Probability and Statistics

Program

1. Introduction: definition of time series, the aims of time series analysis, examples of time series. Descriptive analysis: time plot and time series characteristics (trend, cycle, seasonality, residual component).


2. Correlation and stationarity: dependence measure; strong stationarity; 2nd order stationarity;  autocovariance and autocorrelation functions; partial autocorrelation funtion; estimating the mean, the variamce, the autocovariance and autocorrelation functions. Mutivariate time series and cross-correlation.


3. Smoothing methods: moving averages; simple exponential smoothing; double exponential smoothing; triple exponential smoothing.


4. Time series decomposition: additive and multiplicative decomposition models; loess; "Bureau of Census"; STL  (“Seasonal-Trend Decomposition Procedure Based on Loess”); forecasting; measures of forecast accuracy.


5. Stationarity. Linear stationary time series models: autorregressive models (AR); moving average models (MA); mixed models (ARMA); estimation; forecasting; confidence intervals for forecasts.


6. Nonstationary time series models: ARIMA models;  seasonal models; Box-Jenkins methodology for SARIMA model building; model selection criteria;  forecasting.


7. Unit root tests: Dickey-Fuller and Augmented Dickey-Fuller tests; Phillips-Perron tests; KPSS test.

Mandatory literature

Wei, W.W.S.; Time Series Analysis - Univariate and Multivariate Methods, Pearson/Addison-Wesley, 2006. ISBN: 0-321-32216-9
Cryer, Jonathan D.; Time series analysis : with applications in R, 2009
Spyros G. Makridakis; Forecasting. ISBN: 0-471-53233-9
Hyndman, R., Athanasopoulos, G.; Forecasting: Principles and Practice, www.otexts.com/fpp3

Teaching methods and learning activities

Classes; Problem-solving; Analysis of empirical examples with R.

Software

Python
R project

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Trabalho escrito	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

All students are admitted  to the exam.

Calculation formula of final grade

Exam 50% + Project 50%.

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

Examinations or Special Assignments

An oral exam may be required if deemed necessary to clarify integrity issues.

Classification improvement

The student must take the exam and resubmit a project.