Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials

Ying Hao Yu, Hsiao Che Chien, Pi Hui Ting, Jung Yi Jiang, Pei Yin Chen

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

Abstract

Cost estimation is economically critical before starting off a construction project. One of the essential assignments for materials' prices prediction is to control the cost of inventory. Even though the prediction system based on support vector machine (SVM) recently has been emerged as a favourable choice, the prediction accuracy of SVM is usually deteriorated with nonstationary price data. Thus the way to explore workable price prediction still remains a challenge to be resolved for materials' cost control. In this paper, an enhanced online least squares support vector machine (LS-SVM) is proposed to predict the trend of building materials prices. Our design is to incorporate with empirical mode decomposition (EMD) to deconstruct nonlinear and nonstationary data for the set of intrinsic mode functions (IMFs), which are represented in sinusoidlike waveforms. Superior prediction, therefore, can be attained by predicting IMFs with online LS-SVMs. According to our simulation results, proposed EMD designs notably improve prediction accuracy from online LS-SVM and are workable for the cost estimation of building materials.

Original languageEnglish
Title of host publication31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings
EditorsQuang Ha, Ali Akbarnezhad, Xuesong Shen
PublisherUniversity of Technology Sydney
Pages302-308
Number of pages7
ISBN (Electronic)9780646597119
Publication statusPublished - 2014 Jan 1
Event31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Sydney, Australia
Duration: 2014 Jul 92014 Jul 11

Publication series

Name31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings

Other

Other31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014
CountryAustralia
CitySydney
Period14-07-0914-07-11

Fingerprint

Support vector machines
Decomposition
Costs

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Civil and Structural Engineering
  • Building and Construction

Cite this

Yu, Y. H., Chien, H. C., Ting, P. H., Jiang, J. Y., & Chen, P. Y. (2014). Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials. In Q. Ha, A. Akbarnezhad, & X. Shen (Eds.), 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings (pp. 302-308). (31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings). University of Technology Sydney.
Yu, Ying Hao ; Chien, Hsiao Che ; Ting, Pi Hui ; Jiang, Jung Yi ; Chen, Pei Yin. / Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials. 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings. editor / Quang Ha ; Ali Akbarnezhad ; Xuesong Shen. University of Technology Sydney, 2014. pp. 302-308 (31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings).
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abstract = "Cost estimation is economically critical before starting off a construction project. One of the essential assignments for materials' prices prediction is to control the cost of inventory. Even though the prediction system based on support vector machine (SVM) recently has been emerged as a favourable choice, the prediction accuracy of SVM is usually deteriorated with nonstationary price data. Thus the way to explore workable price prediction still remains a challenge to be resolved for materials' cost control. In this paper, an enhanced online least squares support vector machine (LS-SVM) is proposed to predict the trend of building materials prices. Our design is to incorporate with empirical mode decomposition (EMD) to deconstruct nonlinear and nonstationary data for the set of intrinsic mode functions (IMFs), which are represented in sinusoidlike waveforms. Superior prediction, therefore, can be attained by predicting IMFs with online LS-SVMs. According to our simulation results, proposed EMD designs notably improve prediction accuracy from online LS-SVM and are workable for the cost estimation of building materials.",
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Yu, YH, Chien, HC, Ting, PH, Jiang, JY & Chen, PY 2014, Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials. in Q Ha, A Akbarnezhad & X Shen (eds), 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings. 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, University of Technology Sydney, pp. 302-308, 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014, Sydney, Australia, 14-07-09.

Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials. / Yu, Ying Hao; Chien, Hsiao Che; Ting, Pi Hui; Jiang, Jung Yi; Chen, Pei Yin.

31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings. ed. / Quang Ha; Ali Akbarnezhad; Xuesong Shen. University of Technology Sydney, 2014. p. 302-308 (31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings).

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

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M3 - Conference contribution

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Yu YH, Chien HC, Ting PH, Jiang JY, Chen PY. Enhanced online LS-SVM using EMD algorithm for prices prediction of building materials. In Ha Q, Akbarnezhad A, Shen X, editors, 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings. University of Technology Sydney. 2014. p. 302-308. (31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings).