用TCN修复轨迹数据缺失段，效果优于传统方法。
Inferring Missing Trajectory Data with Temporal Convolutional Networks
- 采用对称膨胀卷积，同时利用前后数据修复缺失轨迹。
- 在20%随机遮蔽的二维轨迹上，R²、MSE、MAE均表现良好。
- 适合处理传感器故障导致的轨迹中断问题。
现实世界中轨迹数据常因传感器故障、通信丢失或遮挡而缺失。本文提出轨迹补全任务：从已有上下文重建连续的缺失片段。我们设计了一种带对称膨胀的时序卷积网络（TCN），打破传统因果性约束，使每个时间步可同时参考过去和未来观测，这一特性对补全至关重要，而传统预测模型不具备。模型采用复合损失函数，包含加权均方误差、边界连续性惩罚和光滑正则项。在包含1000条（训练）、200条（验证）和300条（测试）二维轨迹的合成数据集上，随机遮蔽20%片段，模型取得了良好的R²、MSE和MAE指标。
原文摘要 · Abstract (English)
Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task of \emph{trajectory inpainting}: reconstructing contiguous missing segments from observed context. We propose a Temporal Convolutional Network (TCN) with symmetric dilation that relaxes the standard causality constraint, allowing each time step to draw on both past and future observations, a property that is essential for inpainting, but absent from forecasting-oriented architectures. The model is trained with a composite loss that combines weighted mean squared error, boundary--continuity penalties, and a smoothness regularizer. Trained on a synthetic dataset of $1,000$ (train), $200$ (validation), and $300$ (test) two-dimensional trajectories with randomly placed 20% masked segments, the model achieves good R$^{2}$, MSE and MAE metrics.
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