1D-CNN for High-Frequency Forex Market Volatility Prediction: A Comparative Study

Abstract

This study aims to use one-dimensional convolutional neural network (1D-CNN) to predict high-frequency fluctuations in the foreign exchange market. Through comparative experiments with models such as GRU, LSTM, BiLSTM and CNN-LSTM, the superior performance of 1D-CNN in foreign exchange fluctuation prediction is verified. Experimental results show that 1D-CNN performs best in indicators such as mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2), demonstrating its significant advantages in handling complex fluctuation patterns in the foreign exchange market. This research provides an efficient and accurate technical means for real-time financial market analysis, providing strong support for risk management and investment decisions.