## 2 Related Work  
Integrating diverse data sources through data fusion plays a crucial role in decision-making across domains, including healthcare, image analysis, traffic management, and satellite observation [11; 21; 22; 23]. Traditional fusion methods typically focus on early (preprediction) or late (post-prediction) integration. Due to the non-linear nature of many real-world data, late fusion approaches often yield superior results [13; 24; 25]. The increase in traffic forecasting research highlights the importance of accurate prediction methods given the complex spatio-temporal relationships within traffic systems [26; 21; 27; 28]. These studies highlight the challenges and future directions and advocate for advanced models capable of deciphering complex traffic flow patterns. Moreover, [29] surveys current traffic classification methods, which can be used for data fusion. The methods are classified into five categories: statistics-based, correlation-based, behavior-based, signature-based, and hybrid-based methods. Recent advancements in deep learning have enabled more sophisticated multi-source fusion techniques, particularly for addressing time series data. Early work in this area often focused on combining images and text [30; 31; 32; 33]. To analyze temporal characteristics, the ST-ResNet model [19] combines inputs and external factors to address proximity, periodicity, and trends within crowd traffic data. However, relying on the residual network limits its full exploration of time characteristics. Additionally, it primarily focuses on the correlation between weather and population traffic, potentially missing insights from other influential data sources. Our work extends this concept by incorporating a more comprehensive range of data sources and advanced techniques to enhance the analysis of temporal characteristics in network traffic. Several papers leverage the Telecom Italia Dataset for traffic prediction [20; 34; 35]. The Spatial-Temporal Cross-domain Neural Network (STCNet) employs deep learning and transfer learning to address traffic pattern similarities across different city areas [34]. [PERSON] et al. [35] expand on this approach by introducing a fusion transfer learning method (STC-N) for multiple cross-domain datasets. To further enhance the prediction accuracy, [PERSON] et al. [36] combines reinforcement learning and a Kullback-Leibler (KL) divergence-based Monte-Carlo Q-learning method. Research also focuses on integrating traffic prediction with energy-aware network management strategies. For example, [PERSON] et al. [37] propose the DeepBSC model, which utilizes a geographical and semantic spatial-temporal network (GS-STN) to forecast traffic demand. This prediction informs dynamic base station sleep control, optimizing energy consumption. Similarly, [PERSON] et al. [38] employ a deep Q network (DQN) alongside traffic forecasting to enhance energy efficiency. Transformer-based methods offer a powerful approach to capturing long-range dependencies in time-series data, but various deep learning techniques can further enhance traffic prediction. Several recent studies showcase these advancements. For instance, paper [4] proposes a cognitive controller framework that utilizes artificial intelligence (AI) and machine learning (ML) for intelligent network management. The paper showcases the potential of AI for broader network optimization tasks. However, its focus on a specific use case limits its generalizability. The study by [PERSON] et al. [10] proposes the GLSTTN model for cellular traffic prediction, effectively combining global and local spatial-temporal features using a transformer-based architecture. [PERSON] et al. [39] introduce the CCSANet model, which innovatively integrates a correlation layer in ConvLSTM with a self-attention mechanism, demonstrating improved accuracy over existing methods for extracting spatial features in cellular network traffic prediction. [PERSON] et al.