EMD combined with ensemble of machine learning predictors for foreign exchange rate forecasting

Forecasting foreign exchange rates is a critical financial challenge. In this paper, we build on recent trends and address the limitations of prior research by proposing a novel approach. Our method combines empirical mode decomposition (EMD) with ensemble of machine learning predictors in foreign exchange rate forecasting. To demonstrate that our proposed method (called EMD-ML) is effective, we used the new approach to forecast six foreign exchange rate time series at a specific time. The first experiment was implemented to compare the proposed forecasting model EMD–LSTM, which combines empirical mode decomposition (EMD) with ensemble of Long Short-Term Memory (LSTM) models, and the single LSTM model. The results indicate that the proposed EMD–LSTM model is more effective than the single LSTM. Besides, to aim at comparing deep-learning models against shallow machine learning models in combination with the EMD decomposition, the second experiment compared EMD-LSTM with the approach which combines EMD with an ensemble of k-nearest neighbors’ predictors (called EMD-KNN method) and the results of the second experiment show that EMD-LSTM cannot outperform EMD-KNN in foreign exchange rates forecasting.