Comparative Evaluation of LSTM and GRU for Long-Term Temperature Forecasting: A Case Study

Abstract

Accurate long-term temperature forecasting is essential for climate-sensitive planning in subtropical regions characterized by pronounced seasonal variability. This study compares two recurrent neural network architectures Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for long-term monthly temperature prediction in Taiwan region. Meteorological data from 2010 - 2025 were used, with correlation-based feature selection and Min-Max normalization applied prior to training. The dataset was chronologically partitioned into training, validation, and testing subsets to ensure temporal integrity. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), and convergence behavior was examined to assess computational efficiency. Both models successfully captured nonlinear seasonal dynamics and achieved high forecasting accuracy. However, GRU converged in approximately half the number of training epochs required by LSTM, demonstrating superior computational efficiency. These results suggest that greater architectural complexity does not necessarily yield materially improved long-term forecasting performance. The findings provide empirical evidence to inform recurrent neural network selection for extended-horizon monthly climate prediction tasks.