Data Science / Statistics / R Foundation / Statistics / Time Series Analysis

Statistics - Time Series Analysis


ID 2858612
Classroom 2 days
Webinar 4 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. The course provides tools for empirical work with time series data and is an introduction into the foundation of time series models. It focuses on both univariate and multivariate time series analysis. After completing this course, a student will be able to analyze univariate and multivariate time series data using available software like MS Excel, SPSS and jMulti.

Training Dates

  • 2020-Nov-02 - Nov-05
  • 2021-Jan-11 - Jan-14
  • 2021-Mar-22 - Mar-25
  • 2021-May-31 - Jun-03

590 EUR +VAT

Location | Enrollment


Univariate analysis of time series data

Estimation of the moment-generating functions (expected value, auto-covariance) - auto-correlation: the lag operator, creating and interpretating the correlogram - smoothing of time series data: moving averages, exponential smoothing - transformation and filtering of time series data - first-order and second-order differences

Decomposition of time series using deterministic models

Component models: additive and multiplicative models - seasonal structures in time series: trend, seasons and identification of the seasonal pattern, prognosis and residual analysis - level shifts - linear, parabolic, logistic, exponential fit and regression of time series - polynomials - quality measures

Periodicities in time series

Trigonometric functions and their importance for periodic trends - period detection and frequencies - periodogram: identification and interpretation - regression models with periodic oscillations - spectra and spectral density estimation of time series - introduction to Fourier transformation for time series

Univariate linear time series models using AR(I)MA

Stationarity in time series - White Noise process - AR (Auto Regressive)-models - MA (Moving Average)-models - ARMA and ARIMA models - forecasting - residual analysis - statistical tests for linear time series models - quality measures and model selection

Analysis of multidimensional time series

Cross-correlation and cross-covariance - stationary cross-covariance - co-integration - introduction to cross-spectral analysis and coherence analysis

Multidimensional time series using VAR

VAR (Vector AutoRegressive) processes: modeling, prediction, residual analysis, quality measures, tests

Time series with exogenous influences

Regression with auto-correlated shocks - intervention analysis - transfer function models


Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.