Leveraging Object Detection for Diverse and Accurate Long-Horizon Events Forecasting
Keywords: Event Sequences, Marked Temporal Point Processes, Long Horizon Forecasting, Object Detection, Optimal Assignment
TL;DR: A novel approach to event sequence modeling inspired by techniques from object detection in computer vision
Abstract: Long-horizon event forecasting is critical across various domains, including retail, finance, healthcare, and social networks. Traditional methods, such as Marked Temporal Point Processes (MTPP), often rely on autoregressive models to predict multiple future events. However, these models frequently suffer from issues like converging to constant or repetitive outputs, which limits their effectiveness and general applicability. To address these challenges, we introduce DeTPP (Detection-based Temporal Point Processes), a novel approach inspired by a matching-based loss function from object detection. DeTPP employs a unique matching-based loss function that selectively prioritizes reliably predictable events, improving the accuracy and diversity of predictions during inference. Our method establishes a new state-of-the-art in long-horizon event forecasting, achieving up to a 77% relative improvement over existing MTPP and next-K methods. Furthermore, DeTPP enhances next-event prediction accuracy by up to 2.7\% on a large transactions dataset and demonstrates high computational efficiency during inference.
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Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 7030
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