基于改进灰色模型的数据预测方法研究

2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE) Pub Date : 2021-10-29 DOI:10.1109/ECICE52819.2021.9645598

Runzhi Liu, Mei Li, Linlin Zhao, Zhongmin Lv, Yu Zhang

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引用次数: 0

摘要

预测方法在一定程度上揭示了预测缩进之间的相互作用，不同的模型有许多对应的方法。为了分析物的内部计算网络，提高预测精度，本文建立了一种混合数据- gm预测模型。结果表明，与传统模型相比，混合方法预测值的相对误差减小到10%以下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research on Data Prediction Method Based on Improved Grey Model

Prediction methods reveal the interaction of predicted indents to a certain extent, and there are many corresponding methods to different models. In order to analyze the internal computing network of things and improve the accuracy of prediction, a hybrid data-GM prediction model is established in this paper. The results show that the relative error of the predicted value is reduced to less than 10% by the hybrid method compared with the traditional model.

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2021 IEEE 3rd Eurasia Conference on IOT, Communication and Engineering (ECICE)

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