Inferring Missing Trajectory Data with Temporal Convolutional Networks

Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task of 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.