Axial Pointer: Global-to-Local Prediction for Mixed-Type Event Sequences

Real-world event sequences often record the next event as a complete set of interacting attributes, including irregular timing, numerical measurements, and categorical labels. Predicting that event is difficult because these output types behave differently, and because a categorical attribute may have thousands of possible values across a dataset even though only a small subset appears in one observed history. We introduce *Axial Pointer* (AP), which predicts all attributes from the same history and learns which earlier events provide useful evidence for each prediction. AP can reuse relevant categories or numerical values from that history while retaining the flexibility to predict when historical reuse is insufficient. In this way, AP uses a common source of historical evidence for numbers and categories without forcing these different outputs to be predicted in the same way. Across five heterogeneous transaction datasets and 23 predicted attributes, AP achieves the best or tied-best average performance on 21 attributes; it also performs best among the available learned comparison methods on all three evaluated categorical attributes with thousands of possible values.