A Comparative Analysis of GRU and LSTM Networks for Interval-Valued Foreign Exchange Market Time Series Forecasting

Abstract

The Foreign Exchange (FX) market’s extreme volatility and non-linearity pose significant challenges for traditional point-valued forecasting models. This study proposes a comprehensive framework for interval-valued time series (ITS) forecasting, which captures intraday volatility by predicting the daily low and high bounds of exchange rates. We conduct a rigorous comparative analysis between two prominent deep learning architectures: Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks. To enhance predictive accuracy, the proposed framework leverages a multi-input configuration to model interval-valued time series, using GRU and LSTM networks to learn the non-linear dynamics and complex temporal relationships between the daily price bounds. Empirical evaluations are performed on the EUR/USD exchange rate and simulated datasets characterized by cyclic means and time-varying variance. The numerical results demonstrate that the GRU architecture consistently outperforms the LSTM, achieving a superior Nash-Sutcliffe Efficiency (NSE) of 0.9526 compared to 0.7507 for real-world low-bound prediction. Furthermore, the GRU exhibits greater robustness in simulation experiments and lower computational complexity. These findings suggest that the simplified gating mechanism of GRU is more effective at capturing the structural integrity of FX price intervals, offering a more reliable decision-support tool for market participants navigating high-uncertainty financial environments.