Paper Citation Record · LEDGER

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.18284.

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

pith.paper-citation-record.v1
2505.18284 v1

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measured 41 of 41 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:50.430369Z

measured 41 of 41 standing notices

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

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

41 of 41 outbound references displayed

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verified fuzzy36
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External citation measurements

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Outbound references

Observation 42d96909-28e3-491d-b5ea-1137be37782c · outbound

This paper cites Gwec global wind report 2019.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Gwec global wind report 2019

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:56.445757Z

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 c1b6d982-b190-4237-bca6-5bd4da0d94ac · outbound

This paper cites Probabilistic and deterministic wind speed forecasting based on non- parametric approaches and wind characteristics information.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Probabilistic and deterministic wind speed forecasting based on non- parametric approaches and wind characteristics information

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:56.289930Z

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 ffa1aadf-a92b-436f-b704-f58ff43c81af · outbound

This paper cites Advanced deep learning approach for probabilis- tic wind speed forecasting.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Advanced deep learning approach for probabilis- tic wind speed forecasting

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:56.131157Z

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 dd6cf3f1-e43b-47bc-909c-d7be2499c62d · outbound

This paper cites A multi-model combination approach for probabilistic wind power forecasting.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A multi-model combination approach for probabilistic wind power forecasting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:56.000919Z

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 a7119ca7-704e-4de3-b311-83fbf476a807 · outbound

This paper cites A multi- state model for exploiting the reserve capability of wind power.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A multi- state model for exploiting the reserve capability of wind power

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.848794Z

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 c76d05eb-78ff-4a8b-af0a-90a2cd21a17c · outbound

This paper cites Expected value and chance constrained stochastic unit commitment ensuring wind power utilization.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Expected value and chance constrained stochastic unit commitment ensuring wind power utilization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.728668Z

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 fcdc14b4-7887-4ead-881c-f8d5e9166b47 · outbound

This paper cites Pareto optimal prediction intervals of electricity price.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Pareto optimal prediction intervals of electricity price

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.611326Z

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 7e946ee8-99b2-42de-a51e-7f60993a1306 · outbound

This paper cites Quantile regression, volume 38.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Quantile regression, volume 38

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.467716Z

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 c480a1c5-647b-4efe-a510-c6e371769b0c · outbound

This paper cites Wind farm power uncertainty quantification using a mean- variance estimation method.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Wind farm power uncertainty quantification using a mean- variance estimation method

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.314660Z

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 c7d84428-8f17-461f-8edd-23e2ede4a7a1 · outbound

This paper cites An optimized mean variance es- timation method for uncertainty quantification of wind power forecasts.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed An optimized mean variance es- timation method for uncertainty quantification of wind power forecasts

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.164455Z

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 abde0cd1-be78-4626-8e76-c07337879df9 · outbound

This paper cites Sparse online warped gaussian process for wind power probabilistic forecasting.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Sparse online warped gaussian process for wind power probabilistic forecasting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.984508Z

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 df93adff-db94-4210-a1bc-ff5743b518d4 · outbound

This paper cites Probabilistic wind speed forecast- ing on a grid based on ensemble model output statistics.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Probabilistic wind speed forecast- ing on a grid based on ensemble model output statistics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.830566Z

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 3ecf9d5f-5bf4-4b4b-a0d8-385104d55d0c · outbound

This paper cites Wind- friendly flexible ramping product design in multi-timescale power sys- tem operations.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Wind- friendly flexible ramping product design in multi-timescale power sys- tem operations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.661002Z

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 75b215ae-8ba7-4ad4-8473-59bfa6f297b4 · outbound

This paper cites A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.506122Z

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 09f8c2ee-cd68-4ae2-830f-a13cca4e6c15 · outbound

This paper cites Direct quantile regression for nonparametric probabilistic forecasting of wind power generation.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Direct quantile regression for nonparametric probabilistic forecasting of wind power generation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.331724Z

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 11fc4cf8-4da0-4a33-8cc9-6ddb8dfae9a6 · outbound

This paper cites A regional wind power probabilistic forecast method based on deep quantile regression.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A regional wind power probabilistic forecast method based on deep quantile regression

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.194967Z

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 f52848cf-27ef-4fb8-97b5-a424492c0037 · outbound

This paper cites Wind power probabilistic forecasting based on combined decomposition and deep learning quantile regression.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Wind power probabilistic forecasting based on combined decomposition and deep learning quantile regression

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:54.045001Z

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 3064de72-4cf9-410a-85ca-c9388fdfb1f5 · outbound

This paper cites Ensemble deep learning- based non-crossing quantile regression for nonparametric probabilistic forecasting of wind power generation.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Ensemble deep learning- based non-crossing quantile regression for nonparametric probabilistic forecasting of wind power generation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.899022Z

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 9df822ff-f781-46ea-a691-3957af42075e · outbound

This paper cites A large-scale multi-objective evolutionary quantile estimation model for wind power probabilistic forecasting.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A large-scale multi-objective evolutionary quantile estimation model for wind power probabilistic forecasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.717869Z

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 d27912ac-58a1-4911-b65b-22526f180d46 · outbound

This paper cites Lower upper bound estimation method for construction of neural network-based prediction intervals.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Lower upper bound estimation method for construction of neural network-based prediction intervals

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.547344Z

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 fafef300-4525-4a47-97ab-692ceb17fd36 · outbound

This paper cites Probabilistic wind power forecasts considering different nwp models.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Probabilistic wind power forecasts considering different nwp models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.381000Z

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 0249ac37-f9e0-459f-b8fd-9b31c3c6e051 · outbound

This paper cites An evolutionary multiobjective knee-based lower upper bound estima- tion method for wind speed interval forecast.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed An evolutionary multiobjective knee-based lower upper bound estima- tion method for wind speed interval forecast

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.189782Z

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 47f5ffb5-b6e7-4fdc-ad85-f36d0f5df71a · outbound

This paper cites Probabilistic wind power forecasting based on spiking neural network.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Probabilistic wind power forecasting based on spiking neural network

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:53.072774Z

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 f8f8a736-0e5b-45a7-a7a4-f6e07882af77 · outbound

This paper cites An intelligent deep learning based prediction model for wind power generation.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed An intelligent deep learning based prediction model for wind power generation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.880684Z

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 73959a16-5899-4c87-8fcb-f035a320b364 · outbound

This paper cites A novel interval estimation framework for wind power forecasting using multi- objective gradient descent optimization.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A novel interval estimation framework for wind power forecasting using multi- objective gradient descent optimization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.701349Z

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 1d40f874-faad-42d2-bd58-3dfd0f5b9cc6 · outbound

This paper cites A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.522127Z

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 2adc8c08-d6bf-4c39-ad39-c9414c8150ce · outbound

This paper cites High-quality prediction intervals for deep learning: A distribution-free, ensembled approach.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed High-quality prediction intervals for deep learning: A distribution-free, ensembled approach

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.401085Z

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 49ecec90-851e-4b61-b2aa-16b73abebcde · outbound

This paper cites A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.225614Z

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 30deea77-ecdc-4d98-8fff-28c6ce8a4808 · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:49.585583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 307c15c7-3f2b-4606-a76f-ad7dc2227304 · outbound

This paper cites Long short-term memory.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Long short-term memory

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:49.648269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d885030-3a9d-4124-9ed9-a1a6baa5b4a6 · outbound

This paper cites Temporal convolutional networks for action segmentation and detection.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Temporal convolutional networks for action segmentation and detection

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:49.718930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab3a8cdb-d032-412a-85c2-b061832249fe · outbound

This paper cites Tube Loss: A Novel Approach for Prediction Interval Estimation.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Tube Loss: A Novel Approach for Prediction Interval Estimation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:37:50.634041Z

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 b4e624a6-a120-4d05-b446-4878d81b5e59 · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:52.061097Z

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 e98f8988-3909-4717-84ac-86a2969dad6b · outbound

This paper cites Mixture density networks.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Mixture density networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.909452Z

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 4b45a3b2-dd20-4749-a344-8dd134cb9c74 · outbound

This paper cites Timegpt-.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Timegpt-

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.718389Z

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 c02b98b9-3b9d-419c-9de6-c0e12b8ea640 · outbound

This paper cites TimeGPT-1.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed TimeGPT-1

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:50.128128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53571796-fff8-4929-9dc0-6b0eb16bf3d6 · outbound

This paper cites Short-term wind speed interval prediction using improved quality-driven loss based gated multi- scale convolutional sequence model.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Short-term wind speed interval prediction using improved quality-driven loss based gated multi- scale convolutional sequence model

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.548767Z

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 efaca6c2-2f2f-4ca0-9ae1-22632ec2f76e · outbound

This paper cites Probabilistic wind power forecasting using optimized deep auto-regressive recur- rent neural networks.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Probabilistic wind power forecasting using optimized deep auto-regressive recur- rent neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.305464Z

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 52d50272-85cf-47fa-a227-00a41215728f · outbound

This paper cites An improved mixture density network via wasserstein distance based adversarial learning for probabilistic wind speed predictions.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed An improved mixture density network via wasserstein distance based adversarial learning for probabilistic wind speed predictions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:51.149309Z

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 f2156e16-9502-4002-875c-2b2c845c0f9a · outbound

This paper cites Improved deep mixture density network for regional wind power probabilistic forecasting.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Improved deep mixture density network for regional wind power probabilistic forecasting

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.960890Z

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 07092fb0-2321-457a-bcb8-7f76e142fb33 · outbound

This paper cites Short-term wind speed and power forecasting using an ensemble of mixture density neural networks.

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed Short-term wind speed and power forecasting using an ensemble of mixture density neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:50.750915Z

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.

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