Application of Data Mining Techniques in Economic Statistics
DOI:
https://doi.org/10.5281/zenodo.15534519Keywords:
data mining techniques, economic statistics, market demand forecasting, macroeconomic prediction, financial risk analysis, time series analysisAbstract
With the rapid advancement of information technology, data mining techniques have become increasingly prevalent across various fields, particularly in economic statistics, where they provide powerful tools for processing and analyzing large volumes of complex economic data. This paper explores the application of data mining in economic statistics. It begins by outlining the fundamental concepts and methods of data mining and examines their specific applications in addressing challenges in economic statistics. The paper elaborates on how techniques such as data preprocessing, classification, regression analysis, clustering, and time series analysis are applied to practical issues like market demand forecasting, macroeconomic prediction, and financial risk analysis. Through case studies, it highlights the advantages and potential of data mining in economic statistics while also addressing challenges such as data privacy, security, and quality issues. Finally, the paper discusses the future prospects of data mining in this field and suggests directions for further research.
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Copyright (c) 2025 Haoran Zhou (Author)
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