Comparing univariate techniques for tender price index forecasting:Box-Jenkins and neural network model
| Autor: | Obuks Ejohwomu, Paulo Cortez, Olalekan Shamsideen Oshodi, Ibukun Oluwadara Famakin |
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| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Multivariate statistics
Business Management and Accounting(all) 0211 other engineering and technologies Social Sciences 02 engineering and technology lcsh:TH1-9745 lcsh:TA177.4-185 lcsh:Engineering economy 021105 building & construction Econometrics Economics Construction economics Reliability (statistics) Box-Jenkins Box–Jenkins Artificial neural network Tender price index Event (computing) Univariate 021107 urban & regional planning General Business Management and Accounting Neural network Price index Forecast lcsh:Building construction Model |
| Zdroj: | Oshodi, O S, Ejohwomu, O A, Famakin, I O & Cortez, P 2017, ' Comparing univariate techniques for tender price index forecasting : Box-Jenkins and neural network model ' Construction Economics and Building, vol 17, no. 3, pp. 109-123 . DOI: 10.5130/AJCEB.v17i3.5524 Construction Economics and Building; Vol 17 No 3 (2017): Construction Economics and Building; 109-123 Oshodi, O S, Ejohwomu, O A, Famakin, I O & Cortez, P 2017, ' Comparing univariate techniques for tender price index forecasting : Box-Jenkins and neural network model ', Construction Economics and Building, vol. 17, no. 3, pp. 109-123 . https://doi.org/10.5130/AJCEB.v17i3.5524 Construction Economics and Building, Vol 17, Iss 3 (2017) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 2204-9029 |
| DOI: | 10.5130/AJCEB.v17i3.5524 |
| Popis: | The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box- Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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