Adaptive and high-precision grey forecasting model

Authors:
Yong-Huang Lin;Pin-Chan Lee;Ta-Peng Chang
Affiliations:
Department of Construction Engineering, National Taiwan University of Science and Technology, 43, Sector 4, Keelung Road, Taipei 106, Taiwan;Department of Construction Engineering, National Taiwan University of Science and Technology, 43, Sector 4, Keelung Road, Taipei 106, Taiwan;Department of Construction Engineering, National Taiwan University of Science and Technology, 43, Sector 4, Keelung Road, Taipei 106, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 5
Cited 4

The fast Fourier transform and its applications

The fast Fourier transform and its applications
Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity

Expert Systems with Applications: An International Journal
Grey prediction on indoor comfort temperature for HVAC systems

Expert Systems with Applications: An International Journal
ARMA lattice identification: a new hereditary algorithm

IEEE Transactions on Signal Processing
A high precision global prediction approach based on local prediction approaches

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Single-step ahead prediction based on the principle of concatenation using grey predictors

Expert Systems with Applications: An International Journal
A novel dynamic progress forecasting approach for construction projects

Expert Systems with Applications: An International Journal
Applying fuzzy grey modification model on inflow forecasting

Engineering Applications of Artificial Intelligence
Forecasting the Imminent Market Shares Pertinent to HTC Corporation Domestically: The Vanguard Regarding Taiwan's Smartphone Industry

International Journal of Technology Diffusion

Quantified Score

Hi-index	12.05

Visualization

Abstract

Although the grey forecasting model has been successfully employed in various fields and demonstrated promising results, literatures show its performance still could be improved. Therefore, a new model named EFGMm(1,1) is proposed in this paper by eliminating the error term resulted from the traditional calculation of background value with an integration equation to substitute for such error term. In addition, Fourier series and exponential smooth technique have also been integrated into the new model to reduce the periodic and stochastic residual errors, respectively. An illustrative example of building material stock index is adopted for demonstration. Results show that the proposed model can increase the prediction accuracy, particularly when the system is instable.