Forecasting the EUR/USD exchange rate using EEMD in combination with LSTM Algorithm


  • Tran Thi Tuan Anh University of Economics Ho Chi Minh City
  • Nguyen Cong Quoc University of Economics Ho Chi Minh City


EUR/USD exchange rate, ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM)


Predicting currency exchange rates, particularly for major currencies such as USD and EUR, poses considerable difficulty owing to the complex nature of financial temporal data. This paper utilizes a combined approach that merges the ensemble empirical mode decomposition (EEMD) technique with the long short-term memory (LSTM) neural network to anticipate the sequence of EUR/USD exchange rates. In this fusion method, the EUR/USD rate is decomposed into several intrinsic mode functions (IMFs), which serve as inputs for the LSTM network to perform predictive analysis. The forecasted exchange rate is derived by aggregating the predicted values of these IMFs. Validation results demonstrate that the EEMD-LSTM combined model significantly outperforms in predicting the closing price of the EUR/USD exchange rate. This finding highlights the potential of the EEMD-LSTM combined algorithm in forecasting other complex financial series.


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Tran Thi Tuan Anh, & Nguyen Cong Quoc. (2024). Forecasting the EUR/USD exchange rate using EEMD in combination with LSTM Algorithm. VNU JOURNAL OF ECONOMICS AND BUSINESS, 4(3), 44.



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