Paper Citation Record · LEDGER

Are Transformers Effective for Time Series Forecasting?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2205.13504.

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

pith.paper-citation-record.v1
2205.13504 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:27:22.003606Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

184
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b72d570d-5627-458e-ac6c-dffa9748cc72 · inbound

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

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers Are Transformers Effective for Time Series Forecasting?

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T19:17:50.714484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9430838d-80ea-4e35-b714-032a28ac0529 · inbound

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

Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping Are Transformers Effective for Time Series Forecasting?

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:34:11.864051Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5548c072-3bed-4be8-bf2e-49bc4641c4e9 · inbound

MoTime: A Dataset Suite for Multimodal Time Series Forecasting cites this paper.

MoTime: A Dataset Suite for Multimodal Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T15:27:22.003606Z

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Observation cce3e92f-e5e3-47f9-9e8b-9cc23f402d39 · inbound

Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks cites this paper.

Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks Are Transformers Effective for Time Series Forecasting?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:07:17.879885Z

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Observation c5e327b6-0ebc-4e4b-b52c-6ea7da6090ca · inbound

Neural Functions for Learning Periodic Signal cites this paper.

Neural Functions for Learning Periodic Signal Are Transformers Effective for Time Series Forecasting?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T04:53:34.533452Z

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Observation 8bd05cc9-e979-4a54-b127-ee9b03df113d · inbound

NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs cites this paper.

NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs Are Transformers Effective for Time Series Forecasting?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:05.891135Z

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Observation 54275ec8-820b-4532-9ceb-02d8d57756db · inbound

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series cites this paper.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Are Transformers Effective for Time Series Forecasting?

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:08.069204Z

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Observation fe73e4e9-97c4-4a67-9f72-f64a9c422326 · inbound

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection cites this paper.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Are Transformers Effective for Time Series Forecasting?

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.045870Z

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Observation 83f98d1a-173b-4d1d-b913-a873ef177a78 · inbound

Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:18.579708Z

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Observation af02d47f-90c7-4cb6-97d2-8850a6c168da · inbound

Time Series Forecasting Through the Lens of Dynamics cites this paper.

Time Series Forecasting Through the Lens of Dynamics Are Transformers Effective for Time Series Forecasting?

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T03:32:01.286212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2bb6f3e-c3f6-4609-bfd5-b604f4a7bd5d · inbound

Foundation Models for Clean Energy Forecasting: A Comprehensive Review cites this paper.

Foundation Models for Clean Energy Forecasting: A Comprehensive Review Are Transformers Effective for Time Series Forecasting?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T11:06:13.754405Z

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Observation e6253eee-1189-420a-9ed3-5ea1bf605098 · inbound

Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants cites this paper.

Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants Are Transformers Effective for Time Series Forecasting?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T23:31:53.095532Z

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Observation 6a8aae2b-61b4-4b8f-868d-44ff8c14f170 · inbound

Agoran: An Agentic Open Marketplace for 6G RAN Automation cites this paper.

Agoran: An Agentic Open Marketplace for 6G RAN Automation Are Transformers Effective for Time Series Forecasting?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T04:33:16.373787Z

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Observation babb7427-5d83-4625-a9c4-c716013f9d82 · inbound

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting cites this paper.

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T13:29:04.398331Z

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Observation 0c1cee91-f441-4a79-b546-f1d1fe338be8 · inbound

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models cites this paper.

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models Are Transformers Effective for Time Series Forecasting?

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:21:23.801117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ca2e8b85-23f2-4946-af09-821ae791428e · inbound

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting cites this paper.

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T13:23:08.856089Z

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Observation 524ee6d3-47c3-4083-a6a0-8926bc45251d · inbound

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis Are Transformers Effective for Time Series Forecasting?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:39:03.384914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f392601-e12e-4970-9060-c210e74c0d3b · inbound

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis Are Transformers Effective for Time Series Forecasting?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:20:29.100465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5672c90d-86a9-49c3-9090-54f511d6c9fc · inbound

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis cites this paper.

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis Are Transformers Effective for Time Series Forecasting?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T20:16:42.047438Z

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Observation 1f6a2550-7a10-4cec-9eb6-3a3281d9d69e · inbound

Neural CDEs as Correctors for Learned Time Series Models cites this paper.

Neural CDEs as Correctors for Learned Time Series Models Are Transformers Effective for Time Series Forecasting?

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:28:41.106712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5dcffab2-e682-452d-8367-b07364cb75f2 · inbound

Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion cites this paper.

Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion Are Transformers Effective for Time Series Forecasting?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T17:44:46.691396Z

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Observation 7cc928e5-7111-405a-bb52-5c9750b2f5e9 · inbound

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting cites this paper.

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 3

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T06:51:15.030121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6dcf020f-4e56-406c-a908-162364a6ecc4 · inbound

Beyond Similarity: Temporal Operator Attention for Time Series Analysis cites this paper.

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T02:47:07.708923Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d7ca42ed-d07a-4de0-8d3c-da8180376ccf · inbound

Beyond Similarity: Temporal Operator Attention for Time Series Analysis cites this paper.

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T14:15:46.544607Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a607093d-8da5-4387-8a49-ac0e000b5fd2 · inbound

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models cites this paper.

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models Are Transformers Effective for Time Series Forecasting?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:56.262198Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26d7d615-6e56-4653-9f9a-87496709d0ed · inbound

GITCO: Gated Inference-Time Context Optimization in TSFMs cites this paper.

GITCO: Gated Inference-Time Context Optimization in TSFMs Are Transformers Effective for Time Series Forecasting?

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:16:47.622970Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 98b90914-1fd8-41b9-abfa-989bd1e96aa7 · inbound

CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation cites this paper.

CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation Are Transformers Effective for Time Series Forecasting?

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:31.516516Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9a24b8d-a71c-4f6b-9cf5-5c77fb9d2e0a · inbound

HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting cites this paper.

HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T11:11:54.746924Z

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Observation da23e7cb-0f39-40e8-a2d3-128aa5a9de51 · inbound

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting cites this paper.

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:18:56.372797Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e262bb30-797b-4a2e-9f3e-475d1a8e47d7 · inbound

Flow-PIN: A Two-Stage Power-Flow-Guided Method for System-Wide Multivariate Profile Inpainting in Distribution Networks cites this paper.

Flow-PIN: A Two-Stage Power-Flow-Guided Method for System-Wide Multivariate Profile Inpainting in Distribution Networks Are Transformers Effective for Time Series Forecasting?

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T16:26:20.583912Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7291e82-ebc1-456f-8812-9535f5d63a39 · inbound

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting cites this paper.

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T10:57:05.491156Z

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Observation a90a0296-16e8-4537-9afa-1ef216be8290 · inbound

Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion cites this paper.

Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion Are Transformers Effective for Time Series Forecasting?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T12:18:14.440620Z

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Observation 226128a0-4117-4845-abc9-ffc0a5cec60e · inbound

Challenges of Explainability in Continual Learning for Time Series Forecasting cites this paper.

Challenges of Explainability in Continual Learning for Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T09:16:52.061159Z

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