With the exponential growth of mobile traffic in 5G networks, accurate forecasting is essential for efficient resource management. This research provides a comparative analysis of time series forecasting models for predicting near-future network traffic. Using a public dataset from a 5G base station in Barcelona, this study evaluates the performance of a traditional statistical model, against deep learning models: a Recurrent Neural Network (RNN), a Long Short-Term Memory (LSTM) and a Google timesFM model. The results demonstrate that while the SARIMAX model struggles to capture near-future traffic demand, the deep learning approaches yield significantly higher predictive accuracy. Specifically, a simple LSTM architecture shows great results, outperforming even a more complex one. However, the timesFM model, in particular, shows the most robust generalization capabilities. Additionally, the models trained on data from one base station do not generalize well to others, highlighting significant differences in traffic characteristics even between geographically close locations. This suggests that while locally trained LSTMs are a powerful tool, future work should focus on developing more adaptive and transferable models, such as those using federated learning or graph neural networks.^p