Abstract
It is an important factor to predict the financial data for the decision making by a number of institutions. First, operating recorders of customers are clustered into different classes’ sets in the process of financial accounts management. Second, customers are organized as time series in our model, and the ARMA model is used to predict the result. Our data comes from YuEBao which is used to verify the above method of preprocessing and k-means clustering which is used to predict the time series for the result here. Compared with the actual data, our method outperforms the other method in accuracy.
W. Chen—A Project Supported by Scientific Research Fund of Sichuan Provincial Education Department under Grant No. 17ZB0002.
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Ning, D., Zhang, S., Chen, W., Yu, X. (2018). Clustering Based Prediction of Financial Data by ARMA Model. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_24
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DOI: https://doi.org/10.1007/978-981-13-1648-7_24
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