  Time series prediction aims to predict future values to help stakeholders
make proper strategic decisions. This problem is relevant in all industries and
areas, ranging from financial data to demand to forecast. However, it remains
challenging for practitioners to select the appropriate model to use for
forecasting tasks. With this in mind, we present a model architecture based on
Graph Neural Networks to provide model recommendations for time series
forecasting. We validate our approach on three relevant datasets and compare it
against more than sixteen techniques. Our study shows that the proposed method
performs better than target baselines and state of the art, including
meta-learning. The results show the relevancy and suitability of GNN as methods
for model recommendations in time series forecasting.
