Isabel Silva Magalhães – Faculty of Engineering of the University of Porto
Summary
The course is organized by CLAD (Portuguese Association for Classification and Data Analysis. This course proposes an introduction to the statistical modeling of time series, based on ARIMA models. The preliminary data analysis, validation and diagnosis, as well as the will be covered topics. All the modeling steps will be demonstrated using the R software, using real and/or simulated data. The main packages available in R, useful for the analysis of time series, will be indicated.
Recipients
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.
Duração e Calendarização
The course will last 6 hours and 30 minutes, from 9:00 to 17:30. The more detailed program is attached. conditioning. The course will work remotely, through the Zoom platform (the link will be sent later) with a minimum and maximum number of 10 and 50 participants, respectively. Instructions on installing R will be sent later.
Investment and Application Deadline
The course is free for CLAD members with the 2021 fee paid, the investment for non-CLAD members is €60. CLAD will issue a certificate of participation. The deadline for registration is April 20, 2021.
Contact
If you are interested in attending this course, the attached registration form should be sent to the following e-mail address: mail@clad.pt. The same contact should be used for any other clarifications.
Best regards,
The Board of CLAD
Course Program
9:00 – 12:30:
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 – 17:30:
Autoregressive processes and moving averages, ARMA, stationary: definition and characterization/identification. Parameter estimation. Tools for validation and diagnosis: residual analysis, parametric resampling. Forecast in ARMA processes. Exemplification/Application in R.
