TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals
Summary: TERI recovers trajectories under irregular sampling without prespecified missing positions, jointly detecting gaps and imputing points. Its RETE Transformer combines learnable Fourier spatiotemporal encoding with transition-pattern and contrastive learning, outperforming baselines. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yile Chen (Nanyang Technological University)
- 2. Gao Cong (Nanyang Technological University)
- 3. Cuauhtemoc Anda (DataSpark)
BibTeX Citation
@article{chen_vldb24,
title = {{TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals}},
author = {Chen, Yile and Cong, Gao and Anda, Cuauhtemoc},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {414--426},
doi = {10.14778/3632093.3632105},
url = {https://doi.org/10.14778/3632093.3632105},
year = {2024}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,381 | Finding Time Period-Based Most Frequent Path in Big Trajectory Data | 2013 | SIGMOD | 0.00010965263 |
| 3,849 | Calibrating Trajectory Data for Similarity-based Analysis | 2013 | SIGMOD | 7.0745507e-05 |
| 9,504 | Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks | 2022 | VLDB | 5.2585442e-05 |
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