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401-4623-00L 4 Credits BSC , DR , MSC , WBZ D-INFK , D-MATH , D-PHYS , D-ITET

Time Series Analysis

Lecturers & Examiners: Prof. Dr. Fadoua Balabdaoui
VVZ CR 3.2

Last Updated: 2026-06-03 00:07:36

Abstract

The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ACVF and ACF, Estimation of trend and seasonal component, Linear processes, ARMA processes, Forecasting and estimation of a missing value, the Innovation Algorithm.

Objective

The goal of the course is to have a a good overview of the different types of time series and the approaches used in their statistical analysis.

Content

This course treats modeling and analysis of time series, that is random variables which change in time. As opposed to the i.i.d. framework, the main feature exibited by time series is the dependence between successive observations. The key topics which will be covered as: Stationarity Autocovariance/Autocorrelation Trend estimation Elimination of seasonality Testing for i.i.d. noise Linear Processes AR and ARMA models Causality and invertibility Forecasting Innovatrion algorithm

Resources

Literature

The main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis

General Information

Language
English
Levels
BSC , DR , MSC , WBZ
Frequency
Every two years

Examination

Type
session examination
Mode
written 120 minutes
Aids
None
The examination of this 2-yearly course is only offered in the two examination sessions directly following the course.

Course Components

Type Title Time & Place Hours
lecture with exercise Time Series Analysis No time listed 2 h weekly

Offered In