Abstract: To improve processing of the multi-event time-series data, information about each event type is aggregated for a group of time bins, such that an event bin embedding represents the occurring events of that type in the time bin. The event bin embedding may be based on an aggregated event value summarizing the values of that event type in the bin and a count of those events. The event bin embeddings across event types and time bins may be combined with an embedding for static data about the data instance and a representation token for input to an encoder. The encoder may apply an event-focused sublayer and a time-focused sublayer that attend to respective dimensions of the encoder. The model may be initially trained with self-supervised learning with time and event masking and then fine-tuned for particular applications.