User Trajectory Prediction Unifying Global and Local Temporal Information

TL;DR

This paper introduces a trajectory prediction model that combines global and local temporal features using MLP, MSCNN, and cross-attention, achieving improved accuracy without increasing inference time.

Contribution

It presents a novel multi-scale neural network architecture that effectively captures multi-resolution temporal patterns for user trajectory prediction.

Findings

Reduces MSE by 5.04% compared to ModernTCN.

Reduces MAE by 4.35% compared to ModernTCN.

Maintains similar inference time as existing models.

Abstract

Trajectory prediction is essential for formulating proactive strategies that anticipate user mobility and support advance preparation. Therefore, how to reduce the forecasting error in user trajectory prediction within an acceptable inference time arises as an interesting issue. However, trajectory data contains both global and local temporal information, complicating the extraction of the complete temporal pattern. Moreover, user behavior occurs over different time scales, increasing the difficulty of capturing behavioral patterns. To address these challenges, a trajectory prediction model based on multilayer perceptron (MLP), multi-scale convolutional neural network (MSCNN), and cross-attention (CA) is proposed. Specifically, MLP is used to extract the global temporal information of each feature. In parallel, MSCNN is employed to extract the local temporal information by modeling interactions among features within a local temporal range. Convolutional kernels with different sizes are used in MSCNN to capture temporal information at multiple resolutions, enhancing the model's adaptability to different behavioral patterns. Finally, CA is applied to fuse the global and local temporal information. Experimental results show that our model reduces mean squared error (MSE) by 5.04% and mean absolute error (MAE) by 4.35% compared with ModernTCN in 12-step prediction, while maintaining similar inference time.