Multi-step forecasting of exchange rate based on multi-scale 1D-CNN and attention mechanisms
Deep learning has advantages in processing time series data.Currently,in the applied research of exchange rate time series,deep learning mainly focuses on single-step prediction,which only uses data from previous time points to predict exchange rate data for the next time point.However,this one-step prediction method often fails to provide sufficient decision-making information for decision makers.At the same time,due to the characteristics of non-stationary and high complexity of exchange rate time series,using traditional deep learning methods for forecasting cannot fully explore the characteristics of exchange rate series.Therefore,this study proposes a multi-step prediction method of exchange rate based on multi-scale one-dimensional convolution neural network(1D-CNN)and attention mechanisms,which adaptively integrates multi-scale features and differentiated time series features of exchange rate at different moments to achieve multi-step prediction of exchange rate.Experiments show that the proposed method has higher prediction accuracy than the benchmark methods such as autoregressive integrated moving average model(ARIMA),support vector regression(SVR),Random Walk(RW),eXtreme gradient boosting(XGBoost),long short term memory(LSTM),etc.,which indicates that the proposed method can provide decision support for foreign exchange market investors.
multi-step forecasting of exchange ratedeep learningmulti-scale 1D-CNNatten-tion mechanisms
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