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基于机器学习LSTM&US模型的消费者信心指数预测研究

• 出版日期:2020-07-25 发布日期:2020-07-15

Research on the Prediction of Consumer Confidence Index Based on Machine Learning LSTM&US Model

Tang Xiaobin Dong Manru Zhang Rui

• Online:2020-07-25 Published:2020-07-15

Abstract: Macroeconomic indicators such as the consumer confidence index have the time-lagging effect and the multidimensional dynamic changes,making it difficult to predict accurately.Based on machine learning Long Short-Term Memory (LSTM) neural network model,combined with big data technology to mine User Search (US) data related to consumer confidence index,this paper builds an LSTM&US prediction model.It is then applied to the long-term,medium-term and short-term forecasting research of China′s consumer confidencen index,and multiple benchmark prediction models are introduced for comparative analysis.The results show that the introduction of User Search data can improve the prediction performance and accuracy of the LSTM neural network model;the LSTM&US prediction model has a good generalization ability,and the prediction effect is stable for different periods. Its prediction performance and prediction accuracy are better than the other six benchmark prediction models(LSTM,SVR&US,RFR&US,BP&US,XGB&US,and LGB&US).The prediction results show that the LSTM&US prediction model proposed in this paper has a certain practical value,and the prediction method provides a new idea of forecast and anticipation for consumer confidence index,enriching the theoretical research of machine learning methods in the field of macroeconomic index prediction.