Forecasting USD/RMB exchange rate using the ICEEMDAN-CNN-LSTM model

Because the exchange rate is essentially a dynamic and nonlinear system, exchange rate forecasting has been one of the most challenging topics in the financial field. This paper proposes a novel idea of "decomposition-reconstruction-integration" to predict exchange rate. First, based on ICEEMDAN, the original sequences are decomposed into multifrequency IMFs. Second, we use t-test to determine the high-frequency IMFs, low-frequency IMFs, and trend sequence and reconstruct the high-frequency IMFs into a new component sequence. Third, we use CNN-LSTM model to predict these components separately and finally get the final prediction result by integration. This paper takes the USD/RMB exchange rate as research object, and the experimental results show that (1) the fluctuations of USD/RMB exchange rate are mainly affected by the trend sequence and low-frequency IMFs and are less affected by high-frequency IMFs. (2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN-CNN-LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. In summary, this paper has certain theoretical significance and application value.