Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Yijian Zhang
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Abstract

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.
基于 DEGWO 算法的消费者信心预测指数模型设计
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。
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