Support Vector Machine Regression for project control forecasting

• A Support Vector Regression approach for time and cost forecasting is presented.
• The SVR is tuned using cross-validation and a grid search procedure.
• The SVR outperforms the other methods when training and test sets coincide.
• Robustness checks are conducted to reveal the SVR's limitations.

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.