A Deep Dive into AI-Based Network Traffic Prediction Using Heterogeneous Real Datasets

Recent studies have highlighted that network traffic may be influenced by various external factors such as weather conditions and user behavior, making it challenging to achieve precise predictions using only historical traffic data. To address this limitation, this study proposes a multivariate time series prediction model that incorporates environmental variables, such as meteorological information, to improve the accuracy of network traffic forecasting. Five deep learning models—RNN, GRU, LSTM, CNN, and Transformer—were evaluated under the same experimental conditions. Performance was assessed using metrics such as MSE, RMSE, MAE, R2, and MAPE. In addition, ANOVA and Tukey HSD post hoc tests were conducted to analyze the statistical significance of performance differences between models, and the contribution of each environmental variable was evaluated using the Permutation Importance method, which demonstrated a significant impact on model performance. Experimental results indicated that the GRU and RNN models achieved the best overall prediction accuracy. Additionally, some weather variables, such as temperature and sunlight duration, positively impacted performance improvement. This study empirically demonstrates the generalization capabilities of simple recurrent architectures and the effectiveness of integrating environmental variables. Furthermore, it suggests future research directions, including cross-domain model adaptation and the application of large language model (LLM)-based time series forecasting frameworks.