Paper Citation Record · LEDGER

Cross-Domain Conditional Diffusion Models for Time Series Imputation

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2506.12412.

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

pith.paper-citation-record.v1
2506.12412 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:57:05.035614Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:56:24.414856Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy33
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adfab3ef-263b-48e9-a34a-989cebfe6765 · outbound

This paper cites Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models.

Cross-Domain Conditional Diffusion Models for Time Series Imputation Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

Reference 1

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Observation cc02a879-a108-4f4f-9614-54cb4b80a928 · outbound

This paper cites Advances in neural information processing systems31 (2018).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information processing systems31 (2018)

Reference 2

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Observation f189aff5-1b24-49da-816d-f99f17d596b8 · outbound

This paper cites Scientific reports (2018).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Scientific reports (2018)

Reference 3

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Observation 1ddfb702-e227-4dd4-8e31-7d46c146fc39 · outbound

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

Cross-Domain Conditional Diffusion Models for Time Series Imputation Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Reference 4

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Observation 0fed1310-6184-4376-849a-866c3566f872 · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems (2021).

Cross-Domain Conditional Diffusion Models for Time Series Imputation IEEE Transactions on Neural Networks and Learning Systems (2021)

Reference 5

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Observation 3b209750-379e-400a-8c43-46f94257fd80 · outbound

This paper cites Expert Systems with Applications (2023).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Expert Systems with Applications (2023)

Reference 6

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Observation cd18f3fe-938b-476e-979e-b6b1a1e942d1 · outbound

This paper cites TSI-Bench: Benchmarking Time Series Imputation.

Cross-Domain Conditional Diffusion Models for Time Series Imputation TSI-Bench: Benchmarking Time Series Imputation

Reference 7

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Observation cace1bd4-d62b-4be7-9983-65db77a7ce57 · outbound

This paper cites IEEE Transactions on Artificial Intelligence5(3), 1185–1194 (2023) 16 K.

Cross-Domain Conditional Diffusion Models for Time Series Imputation IEEE Transactions on Artificial Intelligence5(3), 1185–1194 (2023) 16 K

Reference 8

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Observation 8a509757-9b0a-409d-854a-80d0a2574f40 · outbound

This paper cites IEEE Transactions on Artificial Intelli- gence (2024).

Cross-Domain Conditional Diffusion Models for Time Series Imputation IEEE Transactions on Artificial Intelli- gence (2024)

Reference 9

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Observation ecc07124-3fe1-4453-ba17-9093e680fc5c · outbound

This paper cites In: International conference on artificial intelligence and statistics.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: International conference on artificial intelligence and statistics

Reference 10

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Observation 88884b48-be83-4d60-8fb1-20ee26ca446c · outbound

This paper cites SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation.

Cross-Domain Conditional Diffusion Models for Time Series Imputation SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation

Reference 11

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This paper cites In: International confer- ence on machine learning.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: International confer- ence on machine learning

Reference 12

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Observation 54ed32bd-2982-4767-ac4a-b8a4b39eb487 · outbound

This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information processing systems33, 6840–6851 (2020)

Reference 13

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This paper cites In: International Conference on Machine Learning.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: International Conference on Machine Learning

Reference 14

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Observation d73a262c-905a-47a1-88f0-2951b3d80735 · outbound

This paper cites In: International Conference on Machine Learning.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: International Conference on Machine Learning

Reference 15

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Observation c9fd2828-a819-4a58-a84f-c9475cbd32bd · outbound

This paper cites Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement.

Cross-Domain Conditional Diffusion Models for Time Series Imputation Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Reference 16

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Observation ef3a3c2c-832e-4286-a03a-c83f3a6c9b89 · outbound

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Cross-Domain Conditional Diffusion Models for Time Series Imputation Unresolved cited work

Reference 17

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Observation 2bded1ed-13a0-4666-8f76-bb9915e96ccd · outbound

This paper cites John Wiley & Sons (2019).

Cross-Domain Conditional Diffusion Models for Time Series Imputation John Wiley & Sons (2019)

Reference 18

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Observation 9b953705-14e2-4e91-93f6-6f1d2635d555 · outbound

This paper cites In: 2023 IEEE 39th International Conference on Data Engineering (ICDE).

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: 2023 IEEE 39th International Conference on Data Engineering (ICDE)

Reference 19

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Observation 83ab10d0-1085-4bd2-996f-3a9d4d247e69 · outbound

This paper cites In: IJCAI (2021).

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: IJCAI (2021)

Reference 20

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Observation 2ce028c7-c41f-4269-88fd-7504a0eb9e49 · outbound

This paper cites Advances in neural information processing systems31(2018).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information processing systems31(2018)

Reference 21

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This paper cites Advances in neural information pro- cessing systems35, 32069–32082 (2022).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information pro- cessing systems35, 32069–32082 (2022)

Reference 22

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This paper cites In: Proceedings of the AAAI con- ference on artificial intelligence.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the AAAI con- ference on artificial intelligence

Reference 23

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Observation 8699a816-fb71-4858-8afa-0f12f11e5df4 · outbound

This paper cites In: Inter- national Conference on Learning Representations (2022).

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Inter- national Conference on Learning Representations (2022)

Reference 24

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Observation ce920145-2552-4e5c-a440-ac1be6015012 · outbound

This paper cites Springer (1982).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Springer (1982)

Reference 25

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Observation 434e4ae1-3237-4923-a1df-0df6eb63d90f · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems35(1), 1341–1351 (2022).

Cross-Domain Conditional Diffusion Models for Time Series Imputation IEEE Transactions on Neural Networks and Learning Systems35(1), 1341–1351 (2022)

Reference 26

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Observation 5b860e18-385d-4492-9bee-295c8dee97b2 · outbound

This paper cites ACM Transactions on Knowledge Discovery from Data17(8), 1–18 (2023) Cross-Domain Conditional Diffusion Models for Time Series Imputation 17.

Cross-Domain Conditional Diffusion Models for Time Series Imputation ACM Transactions on Knowledge Discovery from Data17(8), 1–18 (2023) Cross-Domain Conditional Diffusion Models for Time Series Imputation 17

Reference 27

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Observation a18ad12b-6b2d-421f-9a7c-f4eb90275ee6 · outbound

This paper cites Water Resources Research (2017).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Water Resources Research (2017)

Reference 28

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This paper cites A DIRT-T Approach to Unsupervised Domain Adaptation.

Cross-Domain Conditional Diffusion Models for Time Series Imputation A DIRT-T Approach to Unsupervised Domain Adaptation

Reference 29

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Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Computer vision–ECCV 2016 workshops

Reference 30

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Observation 6b535b9a-742a-476c-8aed-b604e3084f03 · outbound

This paper cites Advances in neural information processing systems34, 24804–24816 (2021).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information processing systems34, 24804–24816 (2021)

Reference 31

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This paper cites Advances in neural information pro- cessing systems30(2017).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural information pro- cessing systems30(2017)

Reference 32

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This paper cites In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.546341Z

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Observation 22fb31ed-da7b-4af8-a607-e63f4cdf7248 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Cross-Domain Conditional Diffusion Models for Time Series Imputation TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 34

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 35

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Observation ea5ec408-a104-4b5b-a7c3-8c873ff9d9cf · outbound

This paper cites Advances in Neural Information Processing Systems 37, 52595–52623 (2024).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in Neural Information Processing Systems 37, 52595–52623 (2024)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.499103Z

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Observation f945e75f-a011-419a-80ef-d3872a847fcf · outbound

This paper cites In: Proceedings of the 25th international joint conference on artificial intelligence (2016).

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the 25th international joint conference on artificial intelligence (2016)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.463550Z

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Observation c24baa97-af7a-45b8-83f2-d8a2234e41be · outbound

This paper cites In: International conference on machine learning (2018).

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: International conference on machine learning (2018)

Reference 38

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This paper cites IEEE Transactions on Biomedical Engineering66(5), 1477–1490 (2018).

Cross-Domain Conditional Diffusion Models for Time Series Imputation IEEE Transactions on Biomedical Engineering66(5), 1477–1490 (2018)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.420197Z

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Observation aa3a514b-5fa5-498e-8618-aee5b5347a03 · outbound

This paper cites In: Proceedings of the AAAI con- ference on artificial intelligence.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the AAAI con- ference on artificial intelligence

Reference 40

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Observation 1bdf8756-9ccf-4f43-ab07-8b4d24993674 · outbound

This paper cites A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation.

Cross-Domain Conditional Diffusion Models for Time Series Imputation A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation

Reference 41

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Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.377852Z

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Observation e456f4de-3716-4af7-afea-1438852066b4 · outbound

This paper cites Advances in neural infor- mation processing systems35, 3988–4003 (2022).

Cross-Domain Conditional Diffusion Models for Time Series Imputation Advances in neural infor- mation processing systems35, 3988–4003 (2022)

Reference 43

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Observation 40d592f3-449e-4f5b-82cb-39a512f4031a · outbound

This paper cites In: Proceed- ings of the AAAI conference on artificial intelligence.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceed- ings of the AAAI conference on artificial intelligence

Reference 44

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Observation b0f5cc2a-5117-41f6-a7c1-5183f7d70418 · outbound

This paper cites In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management.

Cross-Domain Conditional Diffusion Models for Time Series Imputation In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:57:05.309378Z

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Pith citing papers

Observation 46a59c40-6a30-4d36-a76e-ab76ac59a76f · inbound

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability cites this paper.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Cross-Domain Conditional Diffusion Models for Time Series Imputation

Reference 143

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