Paper Citation Record · LEDGER

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting

As of 18 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.02655.

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

pith.paper-citation-record.v1
2505.02655 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:51:52.987707Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy18
unresolved19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02776001-6de6-4a26-93d3-7b94235678fe · outbound

This paper cites Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting

Reference 1

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Observation b7199b6d-f77c-4a0e-8068-b18a40f3654e · outbound

This paper cites Information Fusion97, 101819 (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Information Fusion97, 101819 (2023)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.341646Z

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Observation e6113289-354f-40b5-b4a0-37a5806b7e0a · outbound

This paper cites Advances in Neural Information Pro- cessing Systems35, 16344–16359 (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Advances in Neural Information Pro- cessing Systems35, 16344–16359 (2022)

Reference 3

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Source-reported events for the cited work

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Observation e72a9853-3c23-4f1d-a57f-a3f6f423fdbd · outbound

This paper cites Transactions on Machine Learning Research (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Transactions on Machine Learning Research (2023)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.325752Z

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Observation 3011f9a0-6037-417a-950e-3a749f4f7e21 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 5

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Source-reported events for the cited work

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Observation d8dc87da-fae7-451d-94c9-db9d789371e0 · outbound

This paper cites an unresolved cited work.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.869639Z

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Observation 5d83f0dd-db58-42a7-8707-625aa58bbd8b · outbound

This paper cites Advances in neural information processing systems34, 15908–15919 (2021).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Advances in neural information processing systems34, 15908–15919 (2021)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.873544Z

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Observation 11fbf278-5fa7-43d5-a93f-262ec018ee1a · outbound

This paper cites IEEE Communications Magazine 57(6), 114–119 (2019).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting IEEE Communications Magazine 57(6), 114–119 (2019)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.305362Z

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Observation 7b224d3f-9f1b-48df-bb69-2d23b7176886 · outbound

This paper cites SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

Reference 9

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Observation 42f0d9b9-7f61-4459-81e9-6b467c716574 · outbound

This paper cites AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.883861Z

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Observation a80a483f-fadb-4835-879b-df8a86567468 · outbound

This paper cites In: In- ternational Conference on Learning Representations (2022),https://openreview.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: In- ternational Conference on Learning Representations (2022),https://openreview

Reference 11

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Observation 70f05ac2-a0cd-4fa9-bb0b-75764e1f8e93 · outbound

This paper cites In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.285612Z

Source-reported events for the cited work

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Observation c62b5e86-e9ee-4960-9033-6b92ce159c0c · outbound

This paper cites Reformer: The Efficient Transformer.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Reformer: The Efficient Transformer

Reference 13

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Observation 9970a189-e383-447d-906a-551fac2e9ddb · outbound

This paper cites In: The 41st international ACM SIGIR conference on research & development in information retrieval.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: The 41st international ACM SIGIR conference on research & development in information retrieval

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.897687Z

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Observation 4658f0fb-0b7e-4c3c-a580-9386dff402e9 · outbound

This paper cites In: 2017 IEEE 19th international conference on e- health networking, applications and services (Healthcom).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: 2017 IEEE 19th international conference on e- health networking, applications and services (Healthcom)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.269509Z

Source-reported events for the cited work

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Observation c81e87bb-a08b-4570-a1dd-8a92a8005e86 · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 16

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Observation 68c124de-b44a-4472-b7ce-160dc7fceae1 · outbound

This paper cites Advances in Neural Information Processing Systems35, 5816–5828 (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Advances in Neural Information Processing Systems35, 5816–5828 (2022)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.259374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bfe76abf-6ba9-46be-8bdf-6cb2955bee7e · outbound

This paper cites Pattern Recognition Letters160, 26–33 (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Pattern Recognition Letters160, 26–33 (2022)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.249711Z

Source-reported events for the cited work

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Observation a86cbdb8-63a9-4da0-aa96-bd7a22e523a5 · outbound

This paper cites Pattern Recognition Letters160, 26–33 (2022).https://doi.org/https: //doi.org/10.1016/j.patrec.2022.05.010,https://www.sciencedirect.com/ science/article/pii/S0167865522001623.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Pattern Recognition Letters160, 26–33 (2022).https://doi.org/https: //doi.org/10.1016/j.patrec.2022.05.010,https://www.sciencedirect.com/ science/article/pii/S0167865522001623

Reference 19

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Observation 9e59acc2-11e3-4b25-b6dd-8e0152a4d18c · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting IEEE Transactions on Neural Networks and Learning Systems (2023)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.918524Z

Source-reported events for the cited work

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Observation 46084f1d-223f-4357-b9d6-7793972c2241 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.922243Z

Source-reported events for the cited work

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Observation a7b7263d-9f20-46c9-a7a2-619009cd41ee · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: The Eleventh International Conference on Learning Representations (2022)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.234194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ba6d1c3f-60c1-433f-8c19-a86fa75669e7 · outbound

This paper cites Engineering Applications of Artificial Intelligence122, 106126 (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Engineering Applications of Artificial Intelligence122, 106126 (2023)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.224478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7baa271-0b86-4b55-86d8-e9449df19124 · outbound

This paper cites Nature communi- cations11(1), 5575 (2020).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Nature communi- cations11(1), 5575 (2020)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.213876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ea28318-2201-4f16-b813-983805d2efb8 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 25

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Observation d91c8017-edd9-4f6c-a2e1-2c65da31a7ff · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.204261Z

Source-reported events for the cited work

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Observation 64ba79ac-b8c9-49ae-a17c-d18d25000e15 · outbound

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

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Advances in neural information pro- cessing systems30(2017)

Reference 27

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Observation e520dfc1-50a1-4178-a54b-f5a6718bdc99 · outbound

This paper cites In: The Twelfth International Conference on Learning Representations (2024).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: The Twelfth International Conference on Learning Representations (2024)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.187876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 176cde44-872c-453c-aa17-7c99a66332e0 · outbound

This paper cites In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations

Reference 29

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This paper cites In: The eleventh international conference on learning representations (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: The eleventh international conference on learning representations (2022)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.172375Z

Source-reported events for the cited work

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Observation f53c5e88-51d6-4251-9e9d-0c274df3924b · outbound

This paper cites Flowformer: Linearizing Transformers with Conservation Flows.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Flowformer: Linearizing Transformers with Conservation Flows

Reference 31

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Source-reported events for the cited work

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Observation f90253b8-86b8-494d-9396-31c20513e104 · outbound

This paper cites Advances in neural information processing systems34, 22419–22430 (2021).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Advances in neural information processing systems34, 22419–22430 (2021)

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T00:51:52.967211Z

Source-reported events for the cited work

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Observation b95005c7-8cb5-40e9-b84e-2ca6a638ba6a · outbound

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SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Unresolved cited work

Reference 33

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Observation 1c262e7a-6012-4350-9382-e8a4de3fd53d · outbound

This paper cites Bioinformatics Ad- vances3(1), vbad001 (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Bioinformatics Ad- vances3(1), vbad001 (2023)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.149257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85e2a200-ec1a-4410-8a62-2be71ba42477 · outbound

This paper cites In: The eleventh international conference on learning representations (2022).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: The eleventh international conference on learning representations (2022)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.139336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6246fef7-f72e-42d5-93cb-fa624d7b5bb5 · outbound

This paper cites Machine Intelligence Research20(4), 514–538 (2023).

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting Machine Intelligence Research20(4), 514–538 (2023)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.127970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd353192-7293-42bd-8521-1f2056ea4659 · outbound

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

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: Proceed- ings of the AAAI conference on artificial intelligence

Reference 37

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Observation 10fb61ee-61a5-4341-9cff-fe136f6b9197 · outbound

This paper cites In: International conference on machine learning.

SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting In: International conference on machine learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:51:53.111074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.