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

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.

Citation

Oshodi, O.S. and Lam, K.C. (2018), "Using an Adaptive Neuro-fuzzy Inference System for Tender Price Index Forecasting: A Univariate Approach", Fayek, A.R. (Ed.) Fuzzy Hybrid Computing in Construction Engineering and Management, Emerald Publishing Limited, Leeds, pp. 389-411. https://doi.org/10.1108/978-1-78743-868-220181011