Paper Citation Record · LEDGER

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.10536.

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pith.paper-citation-record.v1
2506.10536 v1

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T04:29:59.714619Z

measured 36 of 36 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation b4e17581-bf2d-44d1-a238-a9c311b72a64 · outbound

This paper cites Electrical interconnectors: Market opportunities, regulatory issues, technology con- siderations and implications for the gb energy sector.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Electrical interconnectors: Market opportunities, regulatory issues, technology con- siderations and implications for the gb energy sector

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:05.663900Z

Source-reported events for the cited work

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Observation d4a9182b-cb23-4923-943e-4da8ed4c0d73 · outbound

This paper cites A hybrid model for multi-day-ahead electricity price forecasting considering price spikes.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows A hybrid model for multi-day-ahead electricity price forecasting considering price spikes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:05.526693Z

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 7c18c3a2-7af3-4de5-b646-c2a704ab3399 · outbound

This paper cites Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:05.404266Z

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 a6eae828-4e6c-4771-87b8-0edf72888e67 · outbound

This paper cites Dynamic pricing strat- egy for electric vehicle charging stations to distribute the congestion and maximize the revenue.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Dynamic pricing strat- egy for electric vehicle charging stations to distribute the congestion and maximize the revenue

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:05.241280Z

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 f6e35df1-4c67-4898-89ad-2d264dc5d1ec · outbound

This paper cites The effect of flow-based market coupling on cross- border exchange volumes and price convergence in central western euro- pean electricity markets.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows The effect of flow-based market coupling on cross- border exchange volumes and price convergence in central western euro- pean electricity markets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:05.058369Z

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 45ebdfa5-e2f5-44b8-84b2-4674f8ab7825 · outbound

This paper cites A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.916673Z

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 84481175-f52f-4087-882d-977aed8d66a0 · outbound

This paper cites Demand response optimization for smart grid integrated buildings: Review of technology enablers land- scape and innovation challenges.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Demand response optimization for smart grid integrated buildings: Review of technology enablers land- scape and innovation challenges

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.761958Z

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Observation 893e543d-a038-4560-96ed-c6aaaffd32f2 · outbound

This paper cites Market models and optimization techniques to support the decision-making on demand response for prosumers.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Market models and optimization techniques to support the decision-making on demand response for prosumers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.581454Z

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 dbb7d99d-5fab-482d-b440-1b6ae7220b13 · outbound

This paper cites Forecasting day-ahead electricity prices: A review of state-of-the-art al- gorithms, best practices and an open-access benchmark.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Forecasting day-ahead electricity prices: A review of state-of-the-art al- gorithms, best practices and an open-access benchmark

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.194958Z

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 ba2ba666-3d60-431d-b374-a723bb0fcf1c · outbound

This paper cites An optimized deep learning approach for forecasting day-ahead electricity prices.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows An optimized deep learning approach for forecasting day-ahead electricity prices

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.010296Z

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 752263d9-ba1a-4d63-8e8f-666b6041fec4 · outbound

This paper cites Day-ahead elec- tricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in nsw, australia.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Day-ahead elec- tricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in nsw, australia

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:03.841198Z

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 b5d890e1-e3f3-4609-946b-bb861941dc5c · outbound

This paper cites A hybrid gru-lightgbm model for day-ahead electricity price forecasting.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows A hybrid gru-lightgbm model for day-ahead electricity price forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:03.663596Z

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 62544092-9ffa-4014-b049-17dd99d3879b · outbound

This paper cites Muyeen, Mohammad Abdul Mannan, and Innocent Kamwa.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Muyeen, Mohammad Abdul Mannan, and Innocent Kamwa

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:03.464941Z

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 09e8a994-c5d1-4162-a3a9-171c5f314690 · outbound

This paper cites An ensemble approach for enhanced day- ahead price forecasting in electricity markets.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows An ensemble approach for enhanced day- ahead price forecasting in electricity markets

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:03.270904Z

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 037c4aae-03ea-4bd6-9daf-4388da9f8fef · outbound

This paper cites Day- ahead electricity price forecasting using artificial intelligence-based algo- rithms.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Day- ahead electricity price forecasting using artificial intelligence-based algo- rithms

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:03.074959Z

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 6a5ae2e9-ae1c-47cc-a354-3d7bdff40753 · outbound

This paper cites Elec- tricity price forecasting on the day-ahead market using machine learning.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Elec- tricity price forecasting on the day-ahead market using machine learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:04.397752Z

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 701b73db-3ac0-45ff-9d07-2321c133a177 · outbound

This paper cites Day-ahead electricity price forecasting strategy based on machine learning and optimization algorithm.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Day-ahead electricity price forecasting strategy based on machine learning and optimization algorithm

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.926474Z

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 f6569abc-bf5f-495b-88a1-f2df55650525 · outbound

This paper cites Electricity price forecasting on day ahead market via a multivariate cnn-lstm model.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Electricity price forecasting on day ahead market via a multivariate cnn-lstm model

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.807148Z

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 f70626d3-cfa7-4a09-ab52-d34a28225886 · outbound

This paper cites Forecasting day-ahead electricity price with artificial neural networks: a comparison of architectures.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Forecasting day-ahead electricity price with artificial neural networks: a comparison of architectures

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.691737Z

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 48c0da2c-5cee-4497-a103-2478b504e757 · outbound

This paper cites Applying machine learning to electricity price forecasting in simulated energy market scenarios.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Applying machine learning to electricity price forecasting in simulated energy market scenarios

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.589513Z

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 1f86a4fe-e888-41af-a1a5-d26d05c1faf7 · outbound

This paper cites Transparency platform, 2024.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Transparency platform, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.502267Z

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 ffeb853f-16ee-40f2-887a-e12269401f89 · outbound

This paper cites Chapter 7 - foundations of neural networks.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Chapter 7 - foundations of neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.383194Z

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 b4a77f72-877a-4279-9693-c5b5ca20f37c · outbound

This paper cites Recurrent neural networks for time series forecasting: Current status and future directions.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Recurrent neural networks for time series forecasting: Current status and future directions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.251732Z

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 d68c5479-8c31-4f94-b684-cd046632ae51 · outbound

This paper cites Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research , pages 117–138.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research , pages 117–138

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:02.076606Z

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 062093ec-c243-4df2-a7be-ffffc13fd771 · outbound

This paper cites Long Short-Term Memory.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Long Short-Term Memory

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.922067Z

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 aae95bc6-5067-40a2-9958-c5bac4630aec · outbound

This paper cites Learning to forget: Continual prediction with lstm.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Learning to forget: Continual prediction with lstm

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.813458Z

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 f255e764-cad8-448a-885f-618a1d39c3f6 · outbound

This paper cites Long short-term memory neural net- works.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Long short-term memory neural net- works

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.694293Z

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 9a8f7ad3-323d-4260-9b9c-15b9e721d378 · outbound

This paper cites Georgilakis.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Georgilakis

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.548452Z

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 e376b863-5da2-41b3-bfc4-883b70215b17 · outbound

This paper cites The benefits of integrat- ing european electricity markets.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows The benefits of integrat- ing european electricity markets

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.427559Z

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 a671b5c4-bf07-4bd5-bbd6-439c28ddb501 · outbound

This paper cites Short term wholesale electricity market designs: A review of identified challenges and promising solutions.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Short term wholesale electricity market designs: A review of identified challenges and promising solutions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.340793Z

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 a2062a5b-34ab-4b51-88ae-63ad4bdeda6a · outbound

This paper cites Forouli et al.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Forouli et al

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:01.196098Z

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 4e0411be-b4cf-4dc3-a2d7-e3d764c662f1 · outbound

This paper cites Renewable energy statistics, 2023.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Renewable energy statistics, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:00.975454Z

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 496ca98f-0c5a-46b6-9a74-4dd080cdd933 · outbound

This paper cites an unresolved cited work.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:30:00.648766Z

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 7ee8a664-6464-45db-b0b7-2a996e122947 · outbound

This paper cites Market’s coupling - price coupling of regions (pcr), 2024.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Market’s coupling - price coupling of regions (pcr), 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:00.321714Z

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 9bf1781e-b681-4db6-9954-03ecf7f1c071 · outbound

This paper cites Balancing market, 2024.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Balancing market, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:00.004426Z

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 16d3b440-b8df-4d82-9d7e-fde7f6ce7f1e · outbound

This paper cites Unsupervised domain adaptation methods for photovoltaic power forecasting.

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows Unsupervised domain adaptation methods for photovoltaic power forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:59.856884Z

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.

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