Improved GM(1,1) model based on Simpson formula and its applications

TL;DR

This paper introduces a novel discrete GM(1,1) model using Simpson formula for background value reconstruction, enhancing forecast accuracy with limited data, and demonstrates its effectiveness through numerical examples and real-world applications.

Contribution

The paper develops a new GM(1,1) model based on Simpson formula, improving accuracy and unbiasedness over existing models, with systematic analysis and practical validation.

Findings

The ${ m GM_{SD}}$(1,1) model achieves higher accuracy in simulations.

The model provides unbiased predictions for homogeneous sequences.

Applied to economic data, it yields accurate forecasts for GDP and freightage.

Abstract

The classical GM(1,1) model is an efficient tool to {make accurate forecasts} with limited samples. But the accuracy of the GM(1,1) model still needs to be improved. This paper proposes a novel discrete GM(1,1) model, named ${\rm GM_{SD}}$(1,1) model, of which the background value is reconstructed using Simpson formula. The expression of the specific time response function is deduced, and the relationship between our model} and the continuous GM(1,1) model with Simpson formula called ${\rm GM_{SC} }$(1,1) model is systematically discussed. The proposed model is proved to be unbiased to simulate the homogeneous exponent sequence. Further, some numerical examples are given to validate the accuracy of the new ${\rm GM_{SD}}$(1,1) model. Finally, this model is used to predict the Gross Domestic Product and the freightage of Lanzhou, and the results illustrate the ${\rm GM_{SD}}$(1,1) model provides accurate prediction.