Abstract
Financial forecasting is an important research direction of financial data mining. In addition to the general and general characteristics of non-linear, non-static and dynamic, economic time series also have other characteristics, such as high noise, irregularities, and sharp peaks and thick tails. Therefore, financial forecasting is more difficult, but at the same time it has broad application value and market prospects. This article focuses on the research on the application value of BD analysis technology in financial forecasting. First, it uses the literature research method to summarize the advantages of BD analysis technology, and then analyzes the application of data mining in financial data. Finally, through experiments, the application of BD technology predicts an opening price trend of stocks, and the experimental results show that the comparison based on the support vector machine model forecast trend and the original data, the overall forecast accuracy is higher, and basically the same as the original data's upward or downward trend, The error of the data is less than 1%.
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Chen, X. (2022). The Application Value of Big Data Analysis Technology in Financial Forecasting. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-030-89508-2_112
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DOI: https://doi.org/10.1007/978-3-030-89508-2_112
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