Paper Citation Record · LEDGER

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

As of 14 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.05452.

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

pith.paper-citation-record.v1
2607.05452 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:41:55.651993Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a491c937-39de-47a2-b0c8-45a1f4a4deda · outbound

This paper cites Chronos: Learning the Language of Time Series.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Chronos: Learning the Language of Time Series

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9516840d-9cb5-4f77-ad6c-9736b3782e2d · outbound

This paper cites Chronos: Learning the Language of Time Series.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Chronos: Learning the Language of Time Series

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation 87e6eab4-72ab-4b88-9fa8-a6887c715cac · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation a5d751ce-944b-41bb-aa7c-5f89ce921f9c · outbound

This paper cites Language Modeling with Gated Convolutional Networks.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Language Modeling with Gated Convolutional Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation 6aaa9b0f-f96e-41b3-8f84-210d6a283a0d · outbound

This paper cites URLhttps://doi.org/10.1145/3580305.3599533.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps://doi.org/10.1145/3580305.3599533

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation 403e775a-9b62-499a-aabd-4103e6ca7740 · outbound

This paper cites URLhttps://doi.org/10.1162/neco.1997.9.8.1735.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps://doi.org/10.1162/neco.1997.9.8.1735

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

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Observation 1045b35e-e755-498f-85f4-991aef5da8bd · outbound

This paper cites FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c724c5-4faa-4ada-8a92-d804a21b8f5a · outbound

This paper cites an unresolved cited work.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beb4351e-b875-4dab-9474-a8192deaf71e · outbound

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

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e455c7dd-b192-4276-b124-37562b7baecd · outbound

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

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation 37f58cc2-76e5-4aa2-ab62-59e8b62ff802 · outbound

This paper cites URLhttps://www.semanticscholar.org/paper/ 09e098f5bc0a187bea27437a78a0e9ae83cf6a3d.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps://www.semanticscholar.org/paper/ 09e098f5bc0a187bea27437a78a0e9ae83cf6a3d

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation eb75f759-b41a-4de5-93f2-6af1ae5513c8 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation c2ef4b00-78bc-4131-9e1c-fcfcc09e72cc · outbound

This paper cites URLhttps://arxiv.org/abs/2104.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps://arxiv.org/abs/2104

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation caf0d8c6-6a31-423a-a2b3-2910cd71064e · outbound

This paper cites DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b466c8a-b324-4569-b0f9-235d66d4960a · outbound

This paper cites DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation 52ed71af-e54d-417a-b351-97e6e001f3d0 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation 698a16c4-f099-4146-a6d3-2f7f5dfc6991 · outbound

This paper cites Training Very Deep Networks.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Training Very Deep Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation dd74a13c-e061-4f25-accd-3f87a9c6860e · outbound

This paper cites URL https://doi.org/10.1109/icdm59182.2024.00105.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URL https://doi.org/10.1109/icdm59182.2024.00105

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e6053b-0a61-45a8-83b2-be4ca5823f02 · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation 999f46e7-79ea-48ad-9453-40cd7b23a0aa · outbound

This paper cites Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation f4fb7019-6f75-41bf-9ac9-a74d98ec7757 · outbound

This paper cites URLhttps://doi.org/10.1109/ access.2026.3695717.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps://doi.org/10.1109/ access.2026.3695717

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-11T19:48:13.086866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation def996da-d458-41d8-aea1-c134510762a5 · outbound

This paper cites URL https://doi.org/10.1609/aaai.v37i9.26317.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URL https://doi.org/10.1609/aaai.v37i9.26317

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation b5ddffcb-f118-4dc9-a592-6f00c24c4408 · outbound

This paper cites URLhttps: //doi.org/10.1609/aaai.v35i12.17325.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates URLhttps: //doi.org/10.1609/aaai.v35i12.17325

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Observation 200292ab-b30d-4136-84ae-d16a17141fc6 · outbound

This paper cites an unresolved cited work.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Unavailable: canonical work link unavailable.

Observation 24c23e6e-d57c-48e4-9bb0-1ea89093bb83 · outbound

This paper cites FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-11T19:41:55.651993Z

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Pith citing papers

No inbound Pith citation observations are available.