Improving Relational Forecasting with Target History

Relational learning, namely machine learning for relational databases, has attracted increased interest in recent years. However, despite the inherently temporal nature of forecasting tasks in relational learning, modeling of the temporal component has received limited attention. In particular, existing models often do not employ an entity’s past target values in a principled way, even though these values may provide a strong predictive signal. In this work, we investigate multiple model-agnostic strategies for incorporating an entity’s target history into predictions. We show that a simple strategy consistently improves the performance of multiple representative models on RelArena-$\alpha$ forecasting tasks, with the best resulting model achieving new state-of-the-art results among open-source model submissions. Finally, we conduct a case study showing how the current typically used evaluation protocol hinders performance on certain datasets by forbidding the use of information between the test threshold and the sample timestamp. We hope our findings will draw the community’s attention to the importance of the temporal component in relational forecasting tasks and contribute to the development of better benchmarking protocols.