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Research on exchange rate forecasting based on deep belief network

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Abstract

Exchange rate forecasting has always been a research hot spot of international finance studies. Deep belief network (DBN) model of deep learning is a new method of predicting the exchange rate data, and the designing of DBN structure and the learning rules of parameters are the most important parts of DBN model. The paper firstly divides the time series data into training and testing sets. By optimizing the DBN parameters, the paper analyses the results of the training analysis and answers how to do node setting. Then, the paper adjusts the number of hidden nodes, inputs nodes and hidden layers, and by using multiple variance analysis, it determines the sensitive range of the node. Finally, the experiments of INR/USD and CNY/USD have proved that compared with the FFNN model, the improved DBN model could better forecast the exchange rate.

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Authors and Affiliations

  1. College of Economics, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jing Zheng

  2. Institute of Innovation and Development, Hangzhou Dianzi University, Hangzhou, 310018, China

    Xiao Fu

  3. Institute of Applied Mathematics, Hangzhou Dianzi University, Hangzhou, 310018, China

    Guijun Zhang

Authors
  1. Jing Zheng
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  2. Xiao Fu
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  3. Guijun Zhang
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Corresponding author

Correspondence to Xiao Fu.

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Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. This work was supported in part by the Scientific Research Starting Foundation of Hangzhou Dianzi University (No. KYS395617020). The author Fu X. also works in the Research Center of Information Technology & Economic and Social Development.

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Zheng, J., Fu, X. & Zhang, G. Research on exchange rate forecasting based on deep belief network. Neural Comput & Applic 31 (Suppl 1), 573–582 (2019). https://doi.org/10.1007/s00521-017-3039-z

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  • DOI: https://doi.org/10.1007/s00521-017-3039-z

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