Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model
Engineering, Construction and Architectural Management
ISSN: 0969-9988
Article publication date: 16 May 2016
Abstract
Purpose
Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).
Design/methodology/approach
Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.
Findings
The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.
Research limitations/implications
The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.
Practical implications
The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.
Originality/value
This is the first study to apply the NNAR model to construction output forecasting research.
Keywords
Acknowledgements
The work described in this paper was fully supported by a grant from City University of Hong Kong (Project No. 7004369).
Citation
Lam, K.C. and Oshodi, O.S. (2016), "Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model", Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 302-322. https://doi.org/10.1108/ECAM-05-2015-0080
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited