Improving project-profit prediction using a two-stage forecasting system

Latin hypercube sampling is widely used in industrial engineering. In the traditional Latin square sampling, engineers often arrange sample points in the feasible domain uniformly. However, in practice, engineers may have some prior information about the sub-domains where the response volatility is relatively large, named the interesting sub-domains. In order to make full use of these information, this paper employed the D-S evidence theory to fuse prior information from different sources/fields. Then we divide the feasible domain into different sub-domains and indicate the interesting sub-domains. For the sample placement, we put more points in these interesting sub-domains and less points in other sub-domains. Finally, we construct the model with the proposed sample points placement approach based on prior information. A case study was conducted to illustrate the proposed method. The case study shows that the proposed method performs better than the traditional model in MSE, MaxE and StdE.

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Improving project-profit prediction using a two-stage forecasting system

Cited by 4 publications

References 35 publications

Guidelines for applied machine learning in construction industry—A case of profit margins estimation

Guidelines for applied machine learning in construction industry—A case of profit margins estimation

Profit margin prediction in sustainable road freight transportation using machine learning

A modified Latin hypercube sampling based on prior information

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