New publication: SVM in Control

Support Vector Machine Regression

Statistical forecasting and control

The research on statistical project control is lifting off with a new publication on the use of Support Vector Machine Regression techniques for improving the accuracy of project forecasting. The paper entitled "Support Vector Machine regression for project control forecasting" written by Mathieu Wauters and Mario Vanhoucke has been published in Automation in Construction.

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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.