HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
This addresses a gap in event forecasting for applications like finance and healthcare, but it is incremental as it focuses on benchmarking and evaluation rather than introducing a new method.
The paper tackles the problem of long-horizon forecasting of multiple future events using Marked Temporal Point Processes, finding that modern approaches often underperform simple statistical baselines and exhibit mode collapse in predictions.
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail, social networks, and healthcare. Marked Temporal Point Processes (MTPP) provide a principled framework to model both the timing and labels of events. However, most existing research focuses on predicting only the next event, leaving long-horizon forecasting largely underexplored. To address this gap, we introduce HoTPP, the first benchmark specifically designed to rigorously evaluate long-horizon predictions. We identify shortcomings in widely used evaluation metrics, propose a theoretically grounded T-mAP metric, present strong statistical baselines, and offer efficient implementations of popular models. Our empirical results demonstrate that modern MTPP approaches often underperform simple statistical baselines. Furthermore, we analyze the diversity of predicted sequences and find that most methods exhibit mode collapse. Finally, we analyze the impact of autoregression and intensity-based losses on prediction quality, and outline promising directions for future research. The HoTPP source code, hyperparameters, and full evaluation results are available at GitHub.