用上下文增强的LSTM预测篮球运动员动态移动，误差最低达1.51米。
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
- 将上下文信息融入LSTM，捕捉运动中的交互与时间依赖关系。
- 在2秒预测范围内，最终位移误差低至1.51米，优于TCNN、GAT和Transformer。
- 模型训练快、数据需求少，适合实时动态场景的轨迹预测。
在信号处理流水线中进行预测对缓解延迟至关重要，尤其在预测如NBA球员等物体的动态运动时。此类任务因体育运动固有的互动性和不可预测性而极具挑战，其中速度与方向的突然变化普遍存在。传统方法如(S)ARIMA(X)、卡尔曼滤波器（KF）和粒子滤波器（PF）难以建模此类场景中的非线性动态。机器学习方法如长短期记忆网络（LSTM）、图神经网络（GNN）和Transformer虽更具灵活性和准确性，但常未能显式捕捉时间依赖性与上下文交互之间的关联，而这在混乱的体育环境中至关重要。本文评估了这些模型并分析其优劣。实验表明，不同输入历史长度、泛化能力及上下文信息整合能力之间存在显著性能权衡。基于机器学习的方法在长达2秒的预测范围内显著优于线性模型。在测试架构中，融合上下文信息的混合LSTM达到最低最终位移误差（FDE）1.51米，优于时序卷积神经网络（TCNN）、图注意力网络（GAT）和Transformer，同时所需数据量和训练时间也少于GAT与Transformer。结果表明，单一架构无法在所有指标上全面领先，强调在快速动态环境如NBA比赛中进行轨迹预测时需考虑任务特异性。
原文摘要 · Abstract (English)
Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses significant challenges due to the inherently interactive and unpredictable nature of sports, where abrupt changes in velocity and direction are prevalent. Traditional approaches, including (S)ARIMA(X), Kalman filters (KF), and Particle filters (PF), often struggle to model the non-linear dynamics present in such scenarios. Machine learning (ML) methods, such as long short-term memory (LSTM) networks, graph neural networks (GNNs), and Transformers, offer greater flexibility and accuracy but frequently fail to explicitly capture the interplay between temporal dependencies and contextual interactions, which are critical in chaotic sports environments. In this paper, we evaluate these models and assess their strengths and weaknesses. Experimental results reveal key performance trade-offs across input history length, generalizability, and the ability to incorporate contextual information. ML-based methods demonstrated substantial improvements over linear models across forecast horizons of up to 2s. Among the tested architectures, our hybrid LSTM augmented with contextual information achieved the lowest final displacement error (FDE) of 1.51m, outperforming temporal convolutional neural network (TCNN), graph attention network (GAT), and Transformers, while also requiring less data and training time compared to GAT and Transformers. Our findings indicate that no single architecture excels across all metrics, emphasizing the need for task-specific considerations in trajectory prediction for fast-paced, dynamic environments such as NBA gameplay.
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