提出高效建模时间序列集合变化的新框架，兼顾速度与精度。
Scalable Permutation-Aware Modeling for Temporal Set Prediction
- 用排列等变与不变变换捕捉集合动态变化
- 训练和推理速度显著提升，性能持平或超越现有模型
- 适合需要实时处理动态集合的场景，如轨迹预测
时间序列集合预测旨在根据先前多个含可变数量元素的集合序列，预测下一时刻将出现的元素。现有方法通常依赖复杂架构，计算开销大，难以扩展。本文提出一种新颖且可扩展的框架，利用排列等变与排列不变变换高效建模集合动态。该方法显著降低训练与推理时间，同时保持竞争力。在多个公开基准上的大量实验表明，本方法在多项评估指标上达到或超过当前最优模型表现，验证了其在实现高效、可扩展的时间序列集合预测中的有效性。
原文摘要 · Abstract (English)
Temporal set prediction involves forecasting the elements that will appear in the next set, given a sequence of prior sets, each containing a variable number of elements. Existing methods often rely on intricate architectures with substantial computational overhead, which hampers their scalability. In this work, we introduce a novel and scalable framework that leverages permutation-equivariant and permutation-invariant transformations to efficiently model set dynamics. Our approach significantly reduces both training and inference time while maintaining competitive performance. Extensive experiments on multiple public benchmarks show that our method achieves results on par with or superior to state-of-the-art models across several evaluation metrics. These results underscore the effectiveness of our model in enabling efficient and scalable temporal set prediction.
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