12 Apr module 12: time series analysis
- module 12: time series analysis
14/09/2020 - 16/09/2020
9:30 am - 12:45 pm
Mon 14/09: 9:30 am - 5:00 pm | online
Tue 15/09: 9:30 am - 5:00 pm | online
Wed 16/09: 11:15 am - 12:45 pm | online
week 2 | register now
Time series occur in a wide range of disciplines, from business, economic and social sciences to biomedical and engineering contexts. Within the 2020-FLAMES Summer School a module "Time Series Analysis and Forecasting" is offered, which treats actual methods for time series analysis, modelling and forecasting. Apart from the traditional methods for trend and seasonal decomposition of time series, more advanced statistical techniques, are discussed and underlying principles are explained. Furthermore, their application on real data using the statistical software R and Rstudio is demonstrated. In this course, you gain insight in current approaches for time series analysis, modelling and forecasting; specifically:
- exploratory time series analysis
- exponential smoothing models (simple, holt, holt-winter)
- box-Jenkins models (ARMA, ARIMA, SARIMA)
You learn to analyze, to model and to validate time series data using relevant statistical software such as R, Minitab or JMP. You learn to use the models obtained for time series analysis forecasting and scenario analysis.
The following topics will be treated:
- introduction and overview
- exploratory data analysis of time series
- models for trend and seasonal decomposition
- exponential smoothing models
- box-Jenkins ARMA, ARIMA and SARIMA models
- model selection, validation and evaluation
- forecasting and scenario analysis
- interpretation and presentation of the results
Background in a specific discipline is not required. Knowledge of basic statistical techniques such as testing, estimation and regression modelling is desirable. You will need a laptop with R (or Rstudio) installed on it.
Dr. Koo Rijpkema is a senior lecturer at the Eindhoven University of Technology. He has a background in applied statistics and engineering, holding a PhD degree in Statistical Physics. His drive is to get students and researchers inspired by modern methods for research data analysis. His main interests are in the fields of Engineering Statistics, Design of Experiments and Predictive Modeling, with a special focus on the responsible use of modern statistical software, such as R, that is now widely available.