Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data

Abstract

The consumer confidence index is a leading indicator of regional socioeconomic development. Forecasting research on it helps to grasp the future economic trends and consumption trends of regional development in advance. The data contained on the Internet in the era of big data can truly and timely reflect the current economic trends. This paper constructs a conceptual framework for the relationship between the consumer confidence index and web search keywords. It employed six machine learning and deep learning models: the BP neural network, the convolutional neural network, support vector regression, random forest, the ELMAN neural network, and the extreme learning machine to predict the consumer confidence index. The study shows that the use of machine learning models has a better prediction effect on the consumer confidence index. Compared with other models, the BP neural network and the convolutional neural network have lower error indicators and higher model accuracy, which helps decision-makers forecast the consumer confidence index. Consumers search for various goods and prices, as well as macroeconomics, to understand the economic conditions of the market, which affects the consumer confidence index and consumption decisions. Therefore, web search data can be used to predict consumer confidence. Future research can be extended to other macro indicator-related prediction studies. It is important to promote market consumption and confidence, improve consumption policies, and promote national prosperity.