TSGFM - Towards a Graph Foundation Model for Time Series Analysis in Network Monitoring
Hamid Latif-Martínez, Pedro Casas, José Suárez‐Varela, Albert Cabellos‐Aparicio, Pere Barlet‐Ros · 2025
We present TSGFM, a Time Series Graph Foundation Model for network monitoring data analysis, based on spatiotemporal Graph Neural Networks (GNN). Inspired by the success of foundation models in achieving generalization and adaptation, TSGFM leverages pretraining on diverse multivariate timeseries (MTS) data from multiple domains to enable effective zero-shot analysis in network monitoring tasks. We compare TSGFM performance against five state-of-the-art AI/ML models in seven zero-shot forecasting scenarios, using five MTS datasets from different domains. Evaluations demonstrate that TSGFM achieves superior performance in six out of seven zero-shot testing scenarios. Most notably, in zero-shot network monitoring analysis, TSGFM surpasses all competing models by at least 18%, even without training on any network monitoring data.