Time series analysis of construction Cost Index using wavelet transformation and a neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Construction Cost Index (CCI) is widely used to analyze the construction cost variation in time. It can convert the present construction cost to the future or to the past. The current practice of the future cost estimation is simply an extrapolation of the past CCI, which often leads to the inaccurate estimation of future construction cost. This paper presents a new CCI forecasting model using wavelet transformation and an artificial neural network. Preliminary tests showed that the proposed method produced the shortterm range of a future CCI with a greater accuracy as well as a higher reliability compared to existing methods.

Original languageEnglish
Title of host publicationAutomation and Robotics in Construction - Proceedings of the 24th International Symposium on Automation and Robotics in Construction
Pages453-456
Number of pages4
Publication statusPublished - 2007 Dec 1
Event24th International Symposium on Automation and Robotics in Construction, ISARC 2007 - Kochi, India
Duration: 2007 Sep 192007 Sep 21

Publication series

NameAutomation and Robotics in Construction - Proceedings of the 24th International Symposium on Automation and Robotics in Construction

Other

Other24th International Symposium on Automation and Robotics in Construction, ISARC 2007
CountryIndia
CityKochi
Period07/9/1907/9/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Building and Construction

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  • Cite this

    Nam, H., Han, S. H., & Kim, H. (2007). Time series analysis of construction Cost Index using wavelet transformation and a neural network. In Automation and Robotics in Construction - Proceedings of the 24th International Symposium on Automation and Robotics in Construction (pp. 453-456). (Automation and Robotics in Construction - Proceedings of the 24th International Symposium on Automation and Robotics in Construction).