A comparative study of Artificial Intelligence methods for project duration forecasting

•We propose 5 AI methods for predicting a project’s duration.•A methodology including PCA, cross-validation and grid search is presented.•A large computational experiment shows the good performance of the AI methods.•A sensitivity analysis reveals the weakness of the proposed methods.

This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.