A Comparative Study on Deep Learning-Based: Temperature Prediction Models: Performance Evaluation of CNN, Transformer and Random Forest

Accurate short-term temperature prediction is of great significance in fields such as agricultural production and disaster prevention and mitigation. This study aims to explore the performance differences among three modelsConvolutional Neural Network (CNN), Transformer, and Random Forest (RF)in short-term temperature prediction tasks, providing a reference for model selection and optimization in meteorological forecasting. Based on the Beijing PM2.5 dataset, the research constructs supervised learning samples through data preprocessing (using the temperature sequence of the past 24 hours as input to predict the temperature at the 25th hour) and trains and evaluates the three models under unified experimental configurations. The results show that all three models can achieve high-precision predictions. Among them, Random Forest performs the best , with significant advantages in error control, noise resistance, and high training efficiency. CNN follows and excels at capturing local short-term fluctuation features. Transformer , although capable of modeling long-range dependencies, performs slightly inferior with the current dataset. The study reveals that traditional machine learning models still have practical value in resource-constrained scenarios, while deep learning models can further improve accuracy when sufficient data is available. Model fusion and the introduction of multiple factors may be future optimization directions.