ENHANCED TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINES INFERENCE MODEL FOR CASH FLOW PREDICTION AND ESTIMATE AT COMPLETION

Min-Yuan Cheng (), Yu-Wei Wu (), Le Trung Dan () and Andreas F. van Roy ()
Additional contact information
Min-Yuan Cheng: Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.
Yu-Wei Wu: Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.
Le Trung Dan: Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Road, Taipei 106, Taiwan, R.O.C.
Andreas F. van Roy: Department of Civil Engineering, Parahyangan Catholic University, Jl. Ciumbuleuit 94, Bandung 40141, West Java, Indonesia

International Journal of Information Technology & Decision Making (IJITDM), 2013, vol. 12, issue 04, 679-710

Abstract: This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model, EFSIMET, was developed particularly to treat construction management problems that contain time series data. EFSIMETis an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMETto treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIMETare proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.

Keywords: Time series; fuzzy logic; weighted support vector machines; fast messy genetic algorithms; cash flow prediction; estimate at completion (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622013500259
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:12:y:2013:i:04:n:s0219622013500259

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622013500259

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().