t.BA.VS.DP.09HS (Data Analysis and Forecasting) 
Module: Data Analysis and Forecasting
This information was generated on: 18 April 2024
No.
t.BA.VS.DP.09HS
Title
Data Analysis and Forecasting
Organised by
T IDP
Credits
4

Description

Version: 4.0 start 01 August 2020
 

Short description

 The module is centered around multiple linear regression and time series analysis. These techniques are at the heart of statistical modelling and thus provide the basis for the analysis and prediction of important variables such as demand for transport, accident numbers, etc.

Module coordinator

 Marcel Dettling (dtli)

Learning objectives (competencies)
Objectives Competences Taxonomy levels
1 The students are familiar with the most important prediction and forecasting methods from regression analysis and time series analysis. K2
2 The students can analyze, interpret and judge predictions and forecasts for variables such as traffic demand, accident numbers, etc. K4
3 The students can produce such predictions and forecasts using suitable software. Furthermore, they can judge the validity of their results. K6
     

Module contents
  • Nonparametric Smoothing: idea and fundamental difference vs. parametric regression, knowledge anduse of the most important smoothing algorithms as an aid for statistical modelling.
  • Simple Linear Regression: model and assumptions, fitting, confidence and prediction intervals, graphical presentation, model extensions by variable transformations.
  • Multiple Linear Regression: model and assumptions, fitting, confidence and prediction intervals, dealing with categorical predictor variables, model diagnostics, collinearity, model interpretation, estimation of prediction accuracy by cross validation.
  • Time Series Analysis: visualization, mathematical concepts, identifying trend and seasonality, time series decomposition, autocorrelation, autoregressive models, exponential smoothing, time series forecasting, point and interval forecasts, forecasting accuracy.

Teaching materials

 The lecturer will provide theory and exercise materials. Recommendations for further literature will be given in the first week of the course.

Supplementary literature

  

Prerequisites

  

Teaching language

 (X) German ( ) English

Part of International Profile

 ( ) Yes (X) No

Module structure

 Type 2a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams
Description Type Form Scope Grade Weighting
Graded assignments
during teaching semester		 midterm
homework		 exam
hand-in		 90min
300min		 grade
grade		 20%
20%
End-of-semester exam final exam exam 90min grade 60%

Remarks

  

Legal basis

 The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.

Note

Course: Datenanalyse und Prognose - Vorlesung
No.
t.BA.VS.DP.09HS.V
Title
Datenanalyse und Prognose - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 01 August 2099.