A Spatiotemporal Forex Trading System Based on a Hybrid Model GAT-LSTM: Forecasting Forex Price Directions

Abstract

Due to the high volatility and complex interdependencies within financial markets, predicting Forex prices becomes a difficult challenge for investors. Furthermore, the traditional trading models struggle to capture those relationships. To address this issue, we introduced a spatiotemporal Forex trading system, GAT-LSTM-based; it is a hybrid approach that combines Graph Attention Network (GAT) with a Long Short-Term Memory (LSTM) network. The GAT component helps to capture spatial dependencies between currencies by constructing a directed graph containing 28 currency pairs alongside commodity stock and US stocks. The strength of the GAT component lies in its ability to dynamically adjust and recalculate the weights of edges over time, which helps our proposed system to adapt to macroeconomic changes, news events, and financial factors that can impact the Forex market status. The LSTM component deals with the nature of time series datasets. It learns temporal interdependencies, allowing our system to detect repeated long-term patterns over time. Experimental results proved that the suggested hybrid model, GAT-LSTM, surpasses both LSTM and GAT separately. By combining both elements and leveraging simultaneously the strength of dynamically modelling spatial dependencies, and the strength of learning long-term temporal patterns, our suggested system became more accurate in forecasting Forex price directions, showing promising results and high accuracy during the validation phase.