基于优化滚动灰色模型的时间序列预测

M. Yeh, Hung-Ching Lu, Ti-Hung Chen
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摘要

为了提高滚动灰色模型的预测精度,本研究采用高斯裸骨差分进化(GBDE)对构建滚动gm的背景值权重和数据点个数进行优化(1,1)。在两个实时序列预测问题上的实验结果表明,本文提出的基于gbde的滚动gm (l,l)在拟合精度和预测精度上都优于传统的滚动gm (l,l)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Forecasting Using Optimized Rolling Grey Model
This study attempts to improve the forecasting accuracy of rolling grey model by applying Gaussian bare-bones differential evolution (GBDE) to optimize the weight of background value and number of data points used to construct a rolling-GM(1,1). Experimental results on two real time series forecasting problems show that the proposed GBDE-based rolling-GM(l,l) outperforms the traditional rolling-GM(l,l) in terms of fitting accuracy and forecasting accuracy.
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