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Article Dans Une Revue IEEE Intelligent Systems Année : 2020

A Deep Coupled LSTM Approach for USD/CNY Exchange Rate Forecasting

Résumé

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macro-level coupling from interactions with economic fundamentals and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled Long Short-Term Memory (LSTM) approach, namely DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this paper is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings.
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Dates et versions

hal-02569273 , version 1 (08-12-2020)

Identifiants

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Wei Cao, Weidong Zhu, Wenjun Wang, Yves Demazeau, Chen Zhang. A Deep Coupled LSTM Approach for USD/CNY Exchange Rate Forecasting. IEEE Intelligent Systems, 2020, 35 (2), pp.45-53. ⟨10.1109/MIS.2020.2977283⟩. ⟨hal-02569273⟩
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