Building Material Price Forecasting Based on Multi-method in China

As building material price is highly correlative to project cost, various method are utilized for price prediction. However, their accuracies and ranges of application are still in question. To increase the prediction reliability, this paper proposes a multi-method-based way and use it to predict the main building material price in Wuhan, China. Various methods, including triple exponential smoothing, grey prediction model(GM (1,1)), grey Verhulst model, polynomial fitting method, are used separately to obtain the optimum one with minimum mean square error and its prediction result is adopted as the final prediction value. The results show that: For the common C10 commercial concrete, the relative error of optimal predicted value is 0.4%; For the common hot rolled round steels, although the overall optimal method is GM (1,1), the polynomial fitting method is most accuracy at some local time points. Therefore, the results fully demonstrate the effectiveness and rationality of multi-method.

[1]  Hyunsoo Kim,et al.  Automated Time-Series Cost Forecasting System for Construction Materials , 2012 .

[2]  N. A. M. Ghani,et al.  Forecasting techniques suitable to estimate unitary charges price indexes of PFI data: Context of northern region Peninsular Malaysia , 2013, 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC).

[3]  Malik Ranasinghe.  Total project cost: a simplified model for decision makers , 1996 .

[4]  Baabak Ashuri,et al.  Time-Series Analysis for Forecasting Asphalt-Cement Price , 2017 .

[5]  Hang Wen,et al.  Research on Application of Improved BP Algorithms in Price Prediction for Highway Engineering Main Material , 2008 .

[6]  Chester G. Wilmot,et al.  Estimating future highway construction costs , 2003 .

[7]  Sou-Sen Leu,et al.  Hybrid Bayesian Fuzzy-Game Model for Improving the Negotiation Effectiveness of Construction Material Procurement , 2015, J. Comput. Civ. Eng..

[8]  Sou-Sen Leu,et al.  Optimize negotiation price in construction procurement using Bayesian Fuzzy Game Model , 2015 .

[10]  N. A. M. Ghani,et al.  Determining the best forecasting model of cement price index in Malaysia , 2012, 2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER).

[11]  Mohammad Ilbeigi,et al.  Generalized Autoregressive Conditional Heteroscedasticity Model to Quantify and Forecast Uncertainty in the Price of Asphalt Cement , 2017 .

[12]  Edem O. P. Akpan,et al.  METHODOLOGY FOR DETERMINING PRICE VARIATION IN PROJECT EXECUTION , 2001 .

[13]  Saadi Bin Ahmad Kamaruddin,et al.  Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia , 2013 .