Time series analysis and forecasting

Modern methods for time series analysis, modelling and forecasting

In analyzing time series one searches for structures and patterns to describe and explain the underlying process. But also for ways to use adequate models fitted to predict future values or to study 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  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, 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 modesl for correlated series (dynamic regression and VARMA models)
  • 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

Intended for

Academics and professionals who have to analyze and predict time series data in their work. The course is also suited for lecturers at universities or colleges of higher education who want to be informed on actual methods for time series analysis.

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

In consultation with the participants this course can be taught in Dutch or English. Practical exercises are in R.

The techniques demonstrated with R, can also be performed in Python and yield comparable results. Participants receive sample data files so that they can reproduce the results using their preferred data analysis software. It is possible to use R from Python and Python from R and combine the advantages of each. Do you want to use Python or discover more possibilities of Python? View our course Python for engineers.

Also take a look at the overview of all data analytics and statistics courses.
Curious about the background of course leader Koo Rijpkema and his vision on the importance of data in the world of technology? Read the interview!

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  • Information
    Trainer: Dhr. Dr. J.J.M. Rijpkema (Technische Universiteit Eindhoven (TU/e))
    Course data: May 8, 10, 15 and 17 2023
    Day format: daycourse
    Location: Aristo Utrecht
    Price: € 1,995.00 ex. vat
    The program can be taught in English on request.
  • Program

    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
    • Analysis in the frequency domain: Spectral analysis and Periodogram
    • Model selection, validation and evaluation
    • Forecasting (both without and with predictor variables) and scenario analysis
    • Interpretation and presentation of the results
  • Reviews
    This course is assessed with a 8.5
    “Deep dive into applied statistics in time series forecasting.”
    Stefan Manders (ING Bank)
    “Intensive course, with practical insight into the theory of time series.”
    employee Coƶperatie VGZ
    “The course content is presented clearly and the many methods have been structured.”
    employee Belastingdienst Utrecht
    “This course is informative in many ways. The theoretical part is intensive.”
    employee Achmea Expertise
    “Informative course, I want to frequently apply this in my daily work.”
    employee NV Nederlandse Gasunie
    “Profound, theoretically substantiated well, with sufficient practical elements.”
    Martijn van Eeten (IHC Holland BV)
    “The course was a very useful introduction on time series analysis and contains many starting points for further study.”
    employee Centraal Planbureau
    “Nice combination of the fundamentals of time series analysis and practical skills.”
    employee Ministerie van Justitie en Veiligheid
    “Nice introduction with a good overview of the basics in time series analysis.”
    Jan Willem Goemans (Provincie Noord-Holland)
    “Well explained, and often references to previously explained parts. Practicing with the material was also provided.”
    Marc de Wolf (ASML Netherlands BV)