While machines that analyze movement work well, models that anticipate are the future of machine learning. Using static relations to predict future movement results in less accurate forecasting because relations between static entities change over time. This technology uses dynamic Neural Relational Inference to predict an outcome for each static relation over time, resulting in more accurate forecasts.