Trade-off Analysis of Efficiency and Accuracy in GRU vs LSTM

College of Science, Yanbian University, Yanji 133002, China

^* Corresponding author: jinjy2024@lzu.edu.cn

Abstract

While both Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks effectively address the vanishing gradient problem in RNNs, a clear trade-off exists between their computational efficiency and predictive accuracy in the absence of clear quantitative guidelines. for model selection. This study presents a systematic evaluation of this trade-off through mathematical analysis and controlled experiments across financial and meteorological forecasting tasks. Our results demonstrate that GRU attains 30–40% faster training speed with 25–26% fewer parameters, while LSTM reduces prediction error by 36.8% in stock volatility forecasting and by 33% in extreme cold wave prediction. Ablation studies further underscore the critical importance of LSTM’s forget gate and GRU’s reset gate. Based on these insights, we propose a hardware-aware decision framework incorporating an efficiency gain coefficient (α) and an accuracy loss coefficient (β), recommending GRU for latency-sensitive edge applications and LSTM for scenarios demanding high accuracy.