{"type":"ScholarlyArticle","title":"Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion","authors":[{"name":"Wenying He","slug":"wenying-he","given":"Wenying","family":"He","affiliation":"School of Artificial Intelligence , Hebei University of Technology , Tianjin , China"},{"name":"Jieling Huang","slug":"jieling-huang","given":"Jieling","family":"Huang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , Tianjin , China"},{"name":"Junhua Gu","slug":"junhua-gu","given":"Junhua","family":"Gu","affiliation":"School of Artificial Intelligence , Hebei University of Technology , Tianjin , China"},{"name":"Ji Zhang","slug":"ji-zhang","given":"Ji","family":"Zhang","affiliation":"University of Southern Queensland , Queensland , Australia"},{"name":"Yude Bai","slug":"yude-bai","given":"Yude","family":"Bai","affiliation":"School of Software , Tiangong University , Tianjin , China"}],"abstract":"Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.","doi":"10.24963/ijcai.2025/320","url":"https://ijcai.org/proceedings/2025/320","pdf":"https://ijcai.org/proceedings/2025/0320.pdf","markdown":"https://ijcai.org/proceedings/2025/320.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/320","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":320,"pages":{"first":2874,"last":2882},"keywords":["Natural Language Processing: NLP: Applications","Machine Learning: ML: Time series and data streams","Data Mining: DM: Big data and scalability","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"METR-LA PEMS-BAY SF Block Point Block Point MAE MAE MAE MSE MAE MSE Mean 53.48±0.00 4578.08±0.00 7.48±0.00 139.54±0.00 7.56±0.00 142.22±0.00 5.46±0.00 87.56±0.00 5.42±0.00 86.59±0.00 DA 50.51±0.00 4416.10±0.00 14.53±0.00 445.08±0.00 14.57±0.00 448.66±0.00 3.30±0.00 43.76±0.00 3.35±0.00 44.50±0.00 KNN 30.21±0.00 2892.31±0.00 7.79±0.00 124.61±0.00 7.88±0.00 129.29±0.00 4.30±0.00 49.90±0.00 4.30±0.00 49.80±0.00 Linear 14.46±0.00 673.92±0.00 3.26±0.00 33.76±0.00 2.43±0.00 14.75±0.00 1.54±0.00 14.14±0.00 0.76±0.00 1.74±0.00 KF 54.09±0.00 4942.26±0.00 16.75±0.00 534.69±0.00 16.66±0.00 529.96±0.00 5.64±0.00 93.19±0.00 5.68±0.00 93.32±0.00 MICE 30.37±0.00 2594.06±7.17 4.22±0.05 51.07±1.25 4.42±0.07 55.07±1.46 2.94±0.02 28.28±0.37 3.09±0.02 31.43±0.41 VAR 15.64±0.09 833.46±13.85 3.11±0.08 28.00±0.76 2.69±0.00 21.10±0.02 2.09±0.10 16.06±0.73 1.30±0.00 6.52±0.01 TRMF 15.46±0.08 1379.05±34.83 2.96±0.00 22.65±0.13 2.86±0.00 20.39±0.02 1.95±0.01 11.21±0.06 1.85±0.00 10.03±00 BATF 15.21±0.27 662.87±29.55 3.56±0.01 35.39±0.03 3.58±0.01 36.05±0.02 2.05±0.00 14.48±0.01 2.05±0.00 14.90±0.06 V-RIN 10.00±0.10 838.05±24.74 6.84±0.17 150.08±6.13 3.96±0.08 49.98±1.30 2.49±0.04 36.12±0.66 1.21±0.03 6.08±0.29 GP-VAE 25.71±0.30 2589.53±59.14 6.55±0.09 122.33±2.05 6.57±0.10 127.26±3.97 2.86±0.15 26.80±2.10 3.41±0.23 38.95±4.16 rGAIN 15.37±0.26 641.92±33.89 2.90±0.0 21.67±0.15 2.83±0.01 20.03±0.09 2.18±0.01 13.96±0.20 1.88±0.02 10.37±0.20 BRITS 14.50±0.35 622.36±65.16 2.34±0.01 17.00±0.14 2.34±0.00 16.46±0.05 1.70±0.01 10.50±0.07 1.47±0.00 7.94±0.03 GRIN 12.08±0.47 523.14±57.17 2.03±0.00 13.26±0.05 1.91±0.00 10.41±0.03 1.14±0.01 6.60±0.10 0.67±0.00 1.55±0.01 CSDI 9.51±0.10 352.46±7.50 1.98±0.00 12.62±0.60 1.79±0.00 8.96±0.08 0.86±0.00 4.39±0.02 0.57±0.00 1.12±0.03 PriSTI 9.03±0.07 310.39±7.03 1.86±0.00 10.70±0.02 1.72±0.00 8.24±0.05 0.78±0.00 3.31±0.01 0.55±0.00 1.03±0.00 CoFILL 8.70±0.06 296.52±7.29 1.67±0.00 9.42±0.00 1.62±0.00 8.01±0.05 0.74±0.00 2.99±0.02 0.57±0.00 1.03±0.00 Improvement 3.65% 4.47% 10.22% 11.96% 5.81% 2.79% 5.13% 9.67% - - References","title":"MAE MAE MAE MSE MAE MSE Mean","authors":"Metr-La Pems-Bay Sf Block, Point Block Point","year":null,"venue":"MAE MAE MAE MSE MAE MSE Mean","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Agafonov, 2020] Anton Agafonov. 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