Time series analysis and forecasting

Modern methods for time series analysis, modelling and forecasting (with R)

In analysing time series one searches for structures and patterns to describe and explain the underlying process. But also for ways to forecast future values, based on adequate models fitted, or to research the effects of alternative scenarios.

Time series occur in a wide range of disciplines, from business, economic and social sciences to biomedical and engineering contexts. This course handles current 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 available for these tasks, both in the time-domain and in the frequency domain are discussed and underlying principles are explained.

Insight in and practice with time series

In this course:

  • You gain insight in current approaches for time series analysis, modelling and forecasting, specifically:
    • Exponential Smoothing models (Simple, Holt, Holt-Winter)
    • Box-Jenkins models (ARMA, ARIMA, SARIMA)
    • Multivariate time series for correlated series (dynamic regression and VARMA models)
  • You learn to analyse, model and validate time series data with the representative statistical software R
  • You learn to use the models obtained for time series analysis forecasting and scenario analysis.

Intended for

Academics, professionals, teachers at universities and higher professional educational institutes, who have to analyse and predict time series data in their work.

Knowledge of basic statistical techniques like testing, estimating and regression modelling is assumed.

At the request of the participants this course can be taught in Dutch or English.

Share this page

  • Information
    Trainer: Dhr. Dr. J.J.M. Rijpkema (Technische Universiteit Eindhoven (TU/e))
    Course data: 18, 25 maart en 1 april 2019
    Price: € 1,890.00 ex. vat
    The program can (partially) be taught in English.
  • Program

    The focus of the course is on the practical applicability of statistical models for time series analysis and forecasting.
    Lectures are alternated with exercises, cases and demonstrations with the statistical software R.
    At the end of the course a number of case studies will be presented and discussed by the participants. 

    The following topics will be treated:

    • Introduction and overview
    • Models for trend and seasonal decomposition
    • Exponential Smoothing models
    • Box-Jenkins ARMA and ARIMA models
    • Analysis in the frequency domain: Spectral analysis and Periodogram
    • Model selection and evaluation
    • Forecasting (both without and with predictor variables). 
    • Interpretation and presentation of the results.
  • Reviews
    This course is assessed with a 8.2
    “Deep dive into applied statistics in time series forecasting.”
    Stefan Manders (ING Bank)
    “Intensive programme, with practical insight into the theory.”
    “The course content is presented clearly and the many methods have been structured.”