Forecasting Events within Temporal Intervals using First Occurrence Distributions

The prediction of events in a specific time interval is of great significance in different fields such as finance, health care and disaster early warning. In addition to common end-toend models, researchers have explored event sequence prediction (a type of multi-label classification models) and time series fragment forecasting (a type of sequence forecasting models) within prediction intervals. However, these approaches often accumulate errors when dealing with larger intervals or require retraining to adapt to the same event occurring within a different interval. To overcome these limitations, we introduce the time to event models into event prediction, and utilize the distributions of events' first occurrences within a broader time domain for making predictions. By focusing on this extended temporal context, our method aims to provide improved predictions for events across different intervals. We evaluate the performance of our proposed approach on a simulated dataset and three realistic datasets representing distinct domains. Our results show that our method performs comparable to end-to-end methods, and due to the nature of the probability density function, it does not necessitate retraining when adjusting interval lengths.