Cross-Validation of Short-Term Productivity Forecasting Methodologies
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Volume 136, Issue 9
Abstract
Frequent and regular prediction of productivity is needed to effectively manage construction operations in progress. This need is proven by significant deviations in productivity between estimated values and actual values, and the dynamic and stochastic changes in productivity over time. Five statistical methodologies that are appropriate for contemporaneous time series were cross validated in the present study. Validation was conducted by comparing the performance of forecasting models constructed using the methodologies. Performance was measured by evaluating the residual sum of squares and the correlation coefficients. As a result, univariate time series analysis was found to be the best-performing methodology. The univariate time series model is particularly beneficial in two respects. Using a single series of contemporaneous productivity data in numeric form, it reduces the effort expended in collecting and analyzing data, and improves the objectivity of analysis.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 9 • September 2010
Pages: 1037 - 1046
Copyright
© 2010 ASCE.
History
Received: Aug 24, 2009
Accepted: Apr 6, 2010
Published online: Aug 13, 2010
Published in print: Sep 2010
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