Paper Citation Record · LEDGER

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2506.08054.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.08054 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:06.680033Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T14:41:18.515733Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T16:30:09.735515Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy33
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 47e59606-a55e-4638-919d-1d497e350f5c · outbound

This paper cites Traffic data imputation using deep convolu- tional neural networks.IEEE Access, 8:104740–104752,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Traffic data imputation using deep convolu- tional neural networks.IEEE Access, 8:104740–104752,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.099279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c125cdf1-d46b-4bb9-98a1-7a750d1bbc1f · outbound

This paper cites Low-rank autoregressive ten- sor completion for spatiotemporal traffic data imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Low-rank autoregressive ten- sor completion for spatiotemporal traffic data imputation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.021465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 197aa43e-b645-4df0-a0b6-3167290558cf · outbound

This paper cites an unresolved cited work.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:07.011649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bef224a6-eed7-4e89-98c4-67707f3f424e · outbound

This paper cites Graph spectral regularized tensor completion for traffic data imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Graph spectral regularized tensor completion for traffic data imputation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.002342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e51f525-8a00-4711-969e-381a7c306bf0 · outbound

This paper cites Saits: Self-attention-based imputation for time series.Expert Systems with Applications, 219:119619,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Saits: Self-attention-based imputation for time series.Expert Systems with Applications, 219:119619,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.991878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d965b80d-96e5-4038-905a-0203c006f5c3 · outbound

This paper cites When spatio-temporal meet wavelets: Disentan- gled traffic forecasting via efficient spectral graph attention networks.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation When spatio-temporal meet wavelets: Disentan- gled traffic forecasting via efficient spectral graph attention networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.982037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92111be6-7072-41a4-827d-512cfcc85bae · outbound

This paper cites Switch transformers: Scaling to trillion param- eter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Switch transformers: Scaling to trillion param- eter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.590412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b57ca665-37aa-424d-abcc-ef5bb79bea6b · outbound

This paper cites Dy- namic graph convolutional recurrent imputation network for spatiotemporal traffic missing data.Knowledge-Based Systems, 261:110188,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Dy- namic graph convolutional recurrent imputation network for spatiotemporal traffic missing data.Knowledge-Based Systems, 261:110188,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.966817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 806b3836-112c-426c-b112-d5d5895954e7 · outbound

This paper cites Effi- cient missing data imputing for traffic flow by considering temporal and spatial dependence.Transportation research part C: emerging technologies, 34:108–120,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Effi- cient missing data imputing for traffic flow by considering temporal and spatial dependence.Transportation research part C: emerging technologies, 34:108–120,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.958085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 966421c8-0278-40af-8259-0f6dfb6ba699 · outbound

This paper cites an unresolved cited work.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:06.939484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d4a90aff-8560-4074-b759-68e2ec90d64a · outbound

This paper cites Cross-city few-shot traffic forecasting via traffic pat- tern bank.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Cross-city few-shot traffic forecasting via traffic pat- tern bank

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.931202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cca8205f-0998-4e05-8b1d-d1de129cd5c7 · outbound

This paper cites CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.608192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242c7b3b-becf-453f-bf9b-81751bdedb0c · outbound

This paper cites Learning to reconstruct missing data from spa- tiotemporal graphs with sparse observations.Advances in Neural Information Processing Systems, 35:32069–32082,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Learning to reconstruct missing data from spa- tiotemporal graphs with sparse observations.Advances in Neural Information Processing Systems, 35:32069–32082,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.922431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cc472ea1-7c63-4a0c-84b9-4ce62b0adeb5 · outbound

This paper cites Missing data: A comparison of neural network and expectation maxi- mization techniques.Current Science, pages 1514–1521,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Missing data: A comparison of neural network and expectation maxi- mization techniques.Current Science, pages 1514–1521,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.913296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f3e2929c-334a-4eac-b9a5-2aec35740b9c · outbound

This paper cites Self-attention graph convolution imputation network for spatio-temporal traffic data.IEEE Transactions on Intelligent Transportation Sys- tems,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Self-attention graph convolution imputation network for spatio-temporal traffic data.IEEE Transactions on Intelligent Transportation Sys- tems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.869384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d795254-95a8-4866-82f8-6312b7be3552 · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.632392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcc85e17-dded-4015-b933-f1e7035b0a6e · outbound

This paper cites Traffic speed imputation with spatio-temporal attentions and cycle-perceptual training.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Traffic speed imputation with spatio-temporal attentions and cycle-perceptual training

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.860772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5dd21c66-4b8a-4c25-9c97-82408495353a · outbound

This paper cites Hrst-lr: a hessian regularization spatio- temporal low rank algorithm for traffic data imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Hrst-lr: a hessian regularization spatio- temporal low rank algorithm for traffic data imputation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.851759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5432aa45-3cbe-4947-aa00-2dfe9ded19a3 · outbound

This paper cites Hierarchical spatio-temporal graph convo- lutional neural networks for traffic data imputation.Infor- mation Fusion, 106:102292,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Hierarchical spatio-temporal graph convo- lutional neural networks for traffic data imputation.Infor- mation Fusion, 106:102292,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.842704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c19c920e-a9fd-480c-b1ad-2c9dcc154c3a · outbound

This paper cites Spatial-temporal traffic data imputation via graph attention convolutional network.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Spatial-temporal traffic data imputation via graph attention convolutional network

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.834053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff9bfca8-bf30-4829-84ed-b10dd81ba387 · outbound

This paper cites St-mvl: Filling missing values in geo-sensory time series data.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation St-mvl: Filling missing values in geo-sensory time series data

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.824999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9f81fdbf-b612-476e-9e73-6da0e70f583f · outbound

This paper cites Gain: Missing data imputation using gen- erative adversarial nets.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Gain: Missing data imputation using gen- erative adversarial nets

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.654029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 372646e9-d15b-4baf-9748-af2447dbd68e · outbound

This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.Advances in neu- ral information processing systems, 29,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Temporal regularized matrix factorization for high-dimensional time series prediction.Advances in neu- ral information processing systems, 29,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.802321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da11d5cd-0ec7-4909-a179-ee5b49d62d59 · outbound

This paper cites Stgan: Spatio- temporal generative adversarial network for traffic data imputation.IEEE Transactions on Big Data, 9(1):200– 211,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Stgan: Spatio- temporal generative adversarial network for traffic data imputation.IEEE Transactions on Big Data, 9(1):200– 211,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.792337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c61f3149-415b-4b46-8d4d-40729afc244e · outbound

This paper cites MoEfication: Transformer Feed-forward Layers are Mixtures of Experts.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.663514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 710ee19b-8651-4bd7-89ec-874493760fb0 · outbound

This paper cites Self-attention graph convo- lution residual network for traffic data completion.IEEE Transactions on Big Data, 9(2):528–541,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Self-attention graph convo- lution residual network for traffic data completion.IEEE Transactions on Big Data, 9(2):528–541,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.783143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67aaddc9-a6de-494d-962d-25d91de64e37 · outbound

This paper cites Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:41:06.712508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c76c47c6-7544-42b6-91c2-06ba742a5a0c · outbound

This paper cites Traffic data imputation and prediction: An efficient realization of deep learning.IEEE Access, 8:46713–46722,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Traffic data imputation and prediction: An efficient realization of deep learning.IEEE Access, 8:46713–46722,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.773952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63f54e64-b81b-4d20-997c-529d6a5eeda5 · outbound

This paper cites Se-gsl: A general and effective graph struc- ture learning framework through structural entropy opti- mization.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Se-gsl: A general and effective graph struc- ture learning framework through structural entropy opti- mization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.764644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f0b00a4a-a042-442f-935f-fa011a4b1ee5 · outbound

This paper cites Multispans: a multi-range spatial-temporal transformer network for traffic forecast via structural entropy optimization.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Multispans: a multi-range spatial-temporal transformer network for traffic forecast via structural entropy optimization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.754836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25b7d83b-0baa-408b-ba5c-ef9a333d3995 · outbound

This paper cites Imputeformer: Low rankness-induced transformers for generalizable spatiotemporal imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Imputeformer: Low rankness-induced transformers for generalizable spatiotemporal imputation

Reference 2007

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.904211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f077920-1444-4280-be33-8a6f18c11bd6 · outbound

This paper cites Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:06.577607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56337f05-8873-4995-a2f0-3332fdf61f5e · outbound

This paper cites Attention is all you need.Ad- vances in Neural Information Processing Systems,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Attention is all you need.Ad- vances in Neural Information Processing Systems,

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.886308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f65be63-d66c-4257-bb78-c581246f5127 · outbound

This paper cites Fine-grained urban flow inference with incomplete data.IEEE Transac- tions on Knowledge and Data Engineering, 35(6):5851– 5864,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Fine-grained urban flow inference with incomplete data.IEEE Transac- tions on Knowledge and Data Engineering, 35(6):5851– 5864,

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.949001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9fb742ce-d3ba-4d1a-bb6a-45f340de85e9 · outbound

This paper cites Deep learning on traffic prediction: Methods, analysis, and future directions.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Deep learning on traffic prediction: Methods, analysis, and future directions

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.816339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ea7b3ea-999a-405d-8684-7142f6b963a1 · outbound

This paper cites Generative- free urban flow imputation.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Generative- free urban flow imputation

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.877946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fdf39b2f-5f60-4136-a2e4-15845e56dfbe · outbound

This paper cites Missing traffic data imputation for artificial intelligence in intelligent trans- portation systems: review of methods, limitations, and challenges.IEEE Access, 11:34080–34093,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Missing traffic data imputation for artificial intelligence in intelligent trans- portation systems: review of methods, limitations, and challenges.IEEE Access, 11:34080–34093,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.078455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d94d6c57-ae91-4885-ba84-fa67692b8338 · outbound

This paper cites A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation.Transportation Re- search Part C: Emerging Technologies, 117:102673,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation.Transportation Re- search Part C: Emerging Technologies, 117:102673,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.032517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e397d2d-c97a-44e5-97b8-df3c1a1d5aa8 · outbound

This paper cites Brits: Bidirectional recurrent im- putation for time series.Advances in neural information processing systems, 31,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Brits: Bidirectional recurrent im- putation for time series.Advances in neural information processing systems, 31,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.089472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed206169-df68-4cbb-87ec-18e78e5b5fdf · outbound

This paper cites Traffic flow imputation using parallel data and generative adversarial networks.IEEE Transactions on In- telligent Transportation Systems, 21(4):1624–1630,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Traffic flow imputation using parallel data and generative adversarial networks.IEEE Transactions on In- telligent Transportation Systems, 21(4):1624–1630,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.042848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f474050-ed2f-4141-83f1-b3bc7875b1f7 · outbound

This paper cites Bayesian temporal factorization for multidimensional time series prediction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):4659–4673,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Bayesian temporal factorization for multidimensional time series prediction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):4659–4673,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:07.054757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de3336b8-8b87-4683-95f5-d9a57e29a265 · outbound

This paper cites an unresolved cited work.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:07.066662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c08b5e23-6756-4cbd-a3b9-180e9f28e440 · outbound

This paper cites mice: Multivariate imputation by chained equations in r.Journal of statistical software, 45:1–67,.

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation mice: Multivariate imputation by chained equations in r.Journal of statistical software, 45:1–67,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:06.895018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 21c4c314-fe3e-4b6f-9993-bb5360f402cf · inbound

Uniform Inductive Spatio-Temporal Kriging cites this paper.

Uniform Inductive Spatio-Temporal Kriging STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation

Reference 33

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Latent-Mark: An Audio Watermark Robust to Neural Codec Compression cites this paper.

Latent-Mark: An Audio Watermark Robust to Neural Codec Compression STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation

Reference 36

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