Using an Adaptive Neuro-fuzzy Inference System for Tender Price Index Forecasting : A Univariate Approach

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationFuzzy Hybrid Computing in Construction Engineering and Management
Subtitle of host publicationTheory and Applications
EditorsAminah Robinson Fayek
PublisherEmerald Publishing Limited
Pages389-411
Edition1st
ISBN (Electronic)978-1-78743-868-2, 978-1-78743-996-2
ISBN (Print)978-1-78743-869-9
Publication statusPublished - 5 Oct 2018

Abstract

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Research Area(s)

  • Adaptive neuro-fuzzy inference system, Box Jenkins, errors, forecasting, tender price index, Time series modelling

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Using an Adaptive Neuro-fuzzy Inference System for Tender Price Index Forecasting : A Univariate Approach. / Oshodi, Olalekan Shamsideen; Lam, Ka Chi.

Fuzzy Hybrid Computing in Construction Engineering and Management : Theory and Applications. ed. / Aminah Robinson Fayek . 1st. ed. Emerald Publishing Limited, 2018. p. 389-411.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)peer-review