Comparative analysis of transformer variants for time series prediction on multiple network functions data

The Network Data Analytics Function (NWDAF) is an integral component of an operator’s communication network, responsible for the automated sensing and analysis of network data. It plays a crucial role throughout the entire lifecycle of network planning, construction, operation, optimization, and management. This study represents a continuation and deepening of previous research efforts, utilizing variants of the Transformer model for time series forecasting. The experimental results underscore the significant research value of the currently popular Transformer and its variant models. It is important to note, however, that the performance of these models can vary depending on the specific application scenarios and data characteristics.