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A Parallel Approach for Decision Trees Learning from Big Data Streams Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Automated Equation Formulation for Causal Loop Diagrams

Authors : Marc Drobek, Wasif Gilani, Thomas Molka, Danielle Soban

Published in: Business Information Systems

Publisher: Springer International Publishing

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Abstract

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

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previous chapter Evaluating New Approaches of Big Data Analytics Frameworks
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Metadata
Title
Automated Equation Formulation for Causal Loop Diagrams
Authors
Marc Drobek
Wasif Gilani
Thomas Molka
Danielle Soban
Copyright Year
2015
Book
Business Information Systems

Print ISBN: 978-3-319-19026-6

Electronic ISBN: 978-3-319-19027-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-19027-3_4

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