An Adaptive Grey Forecasting Model NGM(1, 1, k2) and Its Application for Short-term Traffic Flow Prediction
Authors: 
Ziheng Wu, Cong LiAuthors Info & Claims
Ziheng Wu, Cong LiAuthors Info & ClaimsArticle No.: 143, Pages 1 - 4
Abstract
One of the most important factors that lead to the instability of the traditional grey NGM(1, 1, k2) model is the parameter estimation error. Based on the background value optimization, studying the parameter estimation method is an important method to improve the performance of the grey model. In this paper, a new adpative grey model with quadratic time-varying parameters is put forward in which the formula to improve the background value of the traditional grey NGM(1, 1, k2) model is deduced by using Lagrange mean value theorem, a new kind of adaptve parameter optimized by particle swarm optimization algorithm is introduced, and the optimal constant value in the time response function is derived based on the sum of squares of relative errors. Experimental results have shown the practicality and effectiveness of the presented optimization model.
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Index Terms
- An Adaptive Grey Forecasting Model NGM(1, 1, k2) and Its Application for Short-term Traffic Flow Prediction
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October 2019
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Association for Computing Machinery
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Publication History
Published: 22 October 2019
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- Research-article
- Research
- Refereed limited
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- the science foundation for young scientists of AnHui University of Technology
- Natural Science Foundation of Anhui Province
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CSAE 2019
CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering
October 22 - 24, 2019
Sanya, China
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Overall Acceptance Rate 368 of 770 submissions, 48%
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