Prediction of foreign currency exchange rates using an attention-based long short-term memory network

Abstract

We propose an attention-based LSTM model for predicting forex rates (ALFA). The prediction process consists of three stages. First, an LSTM model captures temporal dependencies within the forex time series. Next, an attention mechanism assigns different weights (importance scores) to the features of the LSTM model’s output. Finally, a fully connected layer generates predictions of forex rates. We conducted comprehensive experiments to evaluate and compare the performance of ALFA against several models used in previous work and against state-of-the-art deep learning models such as temporal convolutional networks (TCN) and Transformer. Experimental results show that ALFA outperforms the baseline models in most cases, across different currency pairs and feature sets, thanks to its attention mechanism that filters out irrelevant or redundant data to focus on important features. ALFA consistently ranks among the top three of the seven models evaluated and ranks first in most cases. We validated the effectiveness of ALFA by applying it to actual trading scenarios using several currency pairs. In these evaluations, ALFA achieves estimated annual return rates comparable to those of professional traders.