Inferring Missing Trajectory Data with Temporal Convolutional Networks

• URL: http://arxiv.org/abs/2607.25147v1
• Date: Mon, 27 Jul 2026 23:46:15 GMT
• Title: Inferring Missing Trajectory Data with Temporal Convolutional Networks
• Abstract summary: We propose a Temporal Convolutional Network (TCN) with symmetric dilation that relaxes the standard causality constraint.<n>The model is trained with a composite loss that combines weighted mean squared error, boundary--continuity penalties, and a smoothness regularizer.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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