基于 DEGWO 算法的消费者信心预测指数模型设计

Yijian Zhang
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摘要

基于中国经济信息网发布的CCI指数,结合DEGWO差分算法和BP神经网络回归;构建了机器学习模式下的DEGWO-BP合成算法,对消费者信心指数进行预测和拟合;实证结果表明,采用DEGWO-BP算法的消费者信心指数累计误差最小,仅为37.8273;模型的平均绝对误差最小,模型的偏离程度最小;极差值最小的模型稳定性最强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm
Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.
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