@conference{10902/13666,
year = {2015},
url = {http://hdl.handle.net/10902/13666},
abstract = {S-curves are usually taken as expression of project progress and have become a requisite tool for project managers through the execution phase. The common methodology for predicting S-curve forecasting models is based on classifying projects into groups and producing a standard S-curve for each group using multiple linear regression techniques. Traditional regression models taken to fit individual projects require a large amount of data and make many strict assumptions regarding statistical distribution of the data. The grey system theory, however, is well suited to study the behavior of a system with incomplete information or limited amount of discrete data. Easy of use and accuracy, two significant criteria for project managers when choosing a forecasting model, are considered two additional attributes of the grey system theory. This paper proposes a residual Grey prediction model to forecast the actual cost and the cost at completion of a project based on the grey system theory. Results show that the accuracy of the forecasting model is highly efficient.},
publisher = {Elsevier},
publisher = {Procedia Computer Science, 2015, 64, 586–593},
title = {A residual Grey prediction model for predicting S-curves in projects},
author = {San Cristóbal Mateo, José Ramón and Correa Ruiz, Francisco J. and González Villa, María Antonia and Díaz Ruiz de Navamuel, Emma and Madariaga Domínguez, Ernesto and Ortega Piris, Andrés and López Soto, Sergio and Trueba Salas, Manuel},
}