SigFormer: A Transformer for Signaling Data Augmentation via Location Reconstruction

Mobile signaling data has been used to discover comprehensive and fine-grained patterns of individual travel activities. However, mobile signaling data often exhibits notable data quality issues caused by systematic factors (e.g., weather, construction, congestion) or individual factors (e.g., low battery, poor reception). Improving the quality of signaling data is a crucial yet challenging task due to irregular spatiotemporal intervals and complex inter-correlations among base stations. In this paper, we formalize the augmentation of signaling data as a base station location reconstruction task. We propose a location-aware transformer structure named SigFormer, which employs self-supervised learning to reconstruct the location information of missing base stations based on the sampled signaling sequence. Specifically, we first design a Continuous Signaling Encoder to model irregularly sampled signaling sequences and encode interval information generated by base station transitions. Then, we learn the Base Station Embedding to describe implicit features of base stations and design a Neighbor Region Encoder to incorporate geographic information as an auxiliary for base station representations. Finally, through an attention-based encoder-decoder framework, we aggregate location information and base station features, employing sequence learning to capture spatiotemporal dependencies and reconstruct base station locations. Experiment results on real-world datasets indicate that our method outperforms existing approaches for location reconstruction. The proposed method originates from practical demands in telecom systems, addressing the challenge of missing and abnormal base station locations by modeling spatiotemporal transition patterns in signaling data. Our approach supports various tasks in practical applications, including imputing missing base station locations and correcting anomalous locations, effectively improving the quality of signaling data.