Code: | 2MADSAD09 | Acronym: | MPST |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Statistics |
Active? | Yes |
Responsible unit: | Agrupamento Científico de Matemática e Sistemas de Informação |
Course/CS Responsible: | Master in Modeling, Data Analysis and Decision Support Systems |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
MADSAD | 3 | Bologna Official Syllabus | 1 | - | 7,5 | 56 | 202,5 |
The aim of this course is to introduce the students to time series analysis methods.
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
1. Introduction: definition of time series, the aims of time series analysis, examples of time series. Descriptive analysis: cronogram, identification and removal of trend and seasonal components, transformations. Fundamentals of stochastic processes: definition; stationarity; weak stationarity; autocovariance and autocorrelation
functions, partial autocorrelation function; linear difference equations. Estimating the mean and the
autocovariance and autocorrelation functions. Measuring the precision of predtions.
2. Exponential smoothing methods. Moving averages. Simple exponential smoothing. Double exponential smoothing. Triple exponential smoothing.
3. Time series decomposition. Decomposition models: additive and multiplicative; Loess; "bureau of census"; STL. Prediction.
4. Stationary linear time series models: autorregressive models (AR), moving average models (MA), ARMA and SARMA models. Models for linear non stationary time series: ARIMA and SARIMA models. Prediction. Box-Jenkins approach to time series analysis: identification, estimation, model checking. Regression and time series.
5. Unit roots tests
5.1 Dickey-Fuller and Augmented Dickey-Fuller tests.
5.2 Phillips-Perron tests.
5.3 KPSS test.
Classes; example classes and laboratory classes.
Designation | Weight (%) |
---|---|
Exame | 50,00 |
Trabalho escrito | 50,00 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Estudo autónomo | 112,00 |
Frequência das aulas | 56,00 |
Trabalho escrito | 34,50 |
Total: | 202,50 |
Exam 50% + Project 50%.
Minimum grade of 7/20 for each of the components.
The student must take the exam and resubmit a project.