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Support vector machine regression for project control forecasting

Wauters, Mathieu
Vanhoucke, Mario
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2014
Journal
Automation in Construction
Book
Publication Volume
47
Publication Issue
November
Publication Begin page
92
Publication End page
106
Publication Number of pages
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Abstract
Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.
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Keywords
Operations & Supply Chain Management, Earned Value Management (EVM), Support Vector Regression (SVR), Prediction
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