Isabel Silva Magalhães – Faculdade de Engenharia da Universidade do Porto
The course is a joint organization of CLAD (Portuguese Association for Classification and Data Analysis) and IST (Department of Mathematics/CEMAT). This course proposes an introduction to the statistical modeling of time series, based on ARIMA models. Preliminary data analysis, validation and diagnosis, as well as prediction will be topics covered. All modeling steps will be demonstrated using the R software, using real and/or simulated data. The main packages available in R, useful for time series analysis, will be indicated.
All potential users of time series (teachers, researchers, students and professionals from other areas) who need to describe, analyze, interpret and model data with temporal correlation. Basic theory will be introduced but emphasis will be given to the application of concepts using R software.
Duration and Scheduling
The course will last 6 hours and 30 minutes, from 10:00 to 18:30. The more detailed program is attached. conditioning. The course will run with a minimum and maximum number of 10 and 25 participants, respectively. Applications not accepted will be sent to future editions of the course. Each participant must bring their own laptop. Investment and Application Deadline. The course is free for CLAD members with the 2016 fee paid, the investment for non-CLAD members is €60. CLAD will issue a certificate of participation. The deadline for registration is May 20, 2016.
If you are interested in attending this course, the attached registration form should be sent to the following e-mail address: email@example.com. The same contact should be used for any other clarifications.
By the Board of CLAD
José Gonçalves Dias
Time series analysis – introduction and applications in R
10:00 – 13:00:
Introduction to time series: fundamental concepts, analysis objectives, examples and bibliography. Main R software packages for time series analysis. stationarity. Dependency measures. Exploratory data analysis. Exemplification/Application in R.
14:30 – 16:30:
Autoregressive processes and moving averages, ARMA, stationary: definition and characterization/identification. Parameter estimation. Exemplification/Application in R.
17:00 – 18:30:
Tools for validation and diagnosis: residual analysis, parametric resampling. Forecast in ARMA processes. Exemplification/Application in R.