Paper Citation Record · LEDGER

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2412.00403.

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

pith.paper-citation-record.v1
2412.00403 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:28:56.577266Z

measured 23 of 23 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

23 of 23 outbound references displayed

verified exact0
verified fuzzy9
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d2b3c75-c24e-418a-9609-f75f916423ca · outbound

This paper cites Brits: bidirectional recurrent imputation for time series, 2018.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Brits: bidirectional recurrent imputation for time series, 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.934819Z

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 32bb30e0-6976-41a1-ab57-a7b3fcd6cb79 · outbound

This paper cites Multiscale-attention masked autoencoder for missing data imputation of wind turbines.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Multiscale-attention masked autoencoder for missing data imputation of wind turbines

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.921984Z

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 4ffd5300-dd46-47f8-b441-08ef9e5e4c2d · outbound

This paper cites Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.907958Z

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 32fac9fa-d6bc-4c4d-a4fb-a13f0677bc1f · outbound

This paper cites Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.894915Z

Source-reported events for the cited work

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Observation 03cb5174-83e7-4565-b468-24b1e97bf433 · outbound

This paper cites Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.881827Z

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 8b9170b8-a0e4-4b0e-9dc2-079fa9c68bb1 · outbound

This paper cites Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.868347Z

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 60203c1f-4999-4fca-99d5-b678a41ab9ce · outbound

This paper cites Sdwpf: A dataset for spatial dynamic wind power forecasting over a large turbine array.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Sdwpf: A dataset for spatial dynamic wind power forecasting over a large turbine array

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.854510Z

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 859eaf47-23e5-4585-874f-d8a160d627df · outbound

This paper cites an unresolved cited work.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:28:56.840191Z

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 781cd2e6-3db8-4d82-a6ba-ca6c7eeda228 · outbound

This paper cites Scarselli, M.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Scarselli, M

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.824489Z

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 2fd02499-ec86-4d8d-9271-900773ece899 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:28:56.811789Z

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 c2eb98d5-54be-4a0e-9c32-2a47460db897 · outbound

This paper cites Scaling Laws for Neural Language Models.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Scaling Laws for Neural Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.518125Z

Source-reported events for the cited work

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Observation 0c0d914c-3afa-4621-97bb-bd252ec417c4 · outbound

This paper cites Segment Anything.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Segment Anything

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.523065Z

Source-reported events for the cited work

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Observation 7570dd21-ecd8-4907-91ae-22782815e0b0 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.530220Z

Source-reported events for the cited work

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Observation 110d57fe-ea2b-436e-91b0-9466504bbbbb · outbound

This paper cites Language Models are Few-Shot Learners.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Language Models are Few-Shot Learners

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.535436Z

Source-reported events for the cited work

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Observation 4049155d-c5dd-45f2-92c6-d01df6cc2db8 · outbound

This paper cites A survey of time series foundation models: Generalizing time series representation with large language model.arXiv e-prints, page arXiv:2405.02358, 2024.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data A survey of time series foundation models: Generalizing time series representation with large language model.arXiv e-prints, page arXiv:2405.02358, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.540482Z

Source-reported events for the cited work

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Observation fe8f2739-20e5-4255-b14b-e4bc723b9628 · outbound

This paper cites Large Language Models Are Zero-Shot Time Series Forecasters.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Large Language Models Are Zero-Shot Time Series Forecasters

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.545216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76189534-b794-4ce1-8490-b7e3eb1a4aea · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.549866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bfe527f-720b-401c-988e-3b8737f2da93 · outbound

This paper cites Are Language Models Actually Useful for Time Series Forecasting?.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Are Language Models Actually Useful for Time Series Forecasting?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.554641Z

Source-reported events for the cited work

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Observation 6d003afa-7af1-4835-bed1-c17091a1eb1f · outbound

This paper cites Timer: Generative Pre-trained Transformers Are Large Time Series Models.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.559191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b657ee5-05cc-41de-aafd-9499ec93eb14 · outbound

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

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.564363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e5b29c0-db36-4bcc-b334-9bc86c761774 · outbound

This paper cites The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.568998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76e8086c-214f-41de-b50b-ecc3d91ab6f6 · outbound

This paper cites What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.573142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e9d58e9-90b6-4e3a-a8ef-65ed50e84451 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:28:56.577266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.