AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability | Paper Citation Record

Observation 8b248331-c0a9-45b7-8df5-75bf0192e991 · outbound

This paper cites Applications of prob- abilistic forecasting in smart grids: A review,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Applications of prob- abilistic forecasting in smart grids: A review,

Reference 1

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This paper cites Data-driven distribution- ally robust optimal power flow for distribution sys- tems,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Data-driven distribution- ally robust optimal power flow for distribution sys- tems,

Reference 3

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This paper cites On the use of probabilistic forecasts in scheduling of renew- able energy sources coupled to storages,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability On the use of probabilistic forecasts in scheduling of renew- able energy sources coupled to storages,

Reference 4

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Observation 754f750a-455b-435e-88ff-0d272818cbae · outbound

This paper cites Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude,

Reference 5

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Observation a56f85a9-3a19-4d87-89ed-a9a30675eaae · outbound

This paper cites Customized uncertainty quantifi- cation of parking duration predictions for EV smart charging,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Customized uncertainty quantifi- cation of parking duration predictions for EV smart charging,

Reference 6

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Observation a6ac626c-147a-4817-ba5e-eaf46e64a523 · outbound

This paper cites Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging,

Reference 7

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This paper cites Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems,

Reference 8

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Observation 2a989742-21f1-4377-ac74-86d5c3e40e52 · outbound

This paper cites A task-based day-ahead load forecasting model for stochastic economic dispatch,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability A task-based day-ahead load forecasting model for stochastic economic dispatch,

Reference 9

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Observation ceb57b87-2b0f-4bff-a8b6-2f1b344ce671 · outbound

This paper cites N-BEATS: Neural basis expansion anal- ysis for interpretable time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability N-BEATS: Neural basis expansion anal- ysis for interpretable time series forecasting,

Reference 10

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Observation 566b477a-a85f-48a8-b7ab-83ac12308cde · outbound

This paper cites Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,

Reference 11

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This paper cites N-HiTS: Neural hierarchical interpo- lation for time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability N-HiTS: Neural hierarchical interpo- lation for time series forecasting,

Reference 12

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Observation 81cb2c24-d651-4291-9057-8afe424ed600 · outbound

This paper cites Forecasting: Theory and prac- tice,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Forecasting: Theory and prac- tice,

Reference 13

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AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 14

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Observation 6532715e-0322-45aa-a2ea-5bd73d62493c · outbound

This paper cites On the generation of probabilistic fore- casts from deterministic models,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability On the generation of probabilistic fore- casts from deterministic models,

Reference 15

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This paper cites A simple method for the construction of empirical confi- dence limits for economic forecasts,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability A simple method for the construction of empirical confi- dence limits for economic forecasts,

Reference 16

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This paper cites Conformal time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Conformal time series forecasting,

Reference 17

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Observation ad59103b-f8a0-42dd-8496-0a8c682702fb · outbound

This paper cites Modeling load forecast uncertainty using generative adver- sarial networks,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Modeling load forecast uncertainty using generative adver- sarial networks,

Reference 18

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Observation ac68a84a-faf6-4618-ad94-e981d2fd1a88 · outbound

This paper cites Generating probabilis- tic forecasts from arbitrary point forecasts using a conditional invertible neural network,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Generating probabilis- tic forecasts from arbitrary point forecasts using a conditional invertible neural network,

Reference 19

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Observation 90788382-a930-4882-964c-2ec30b911e96 · outbound

This paper cites Hutter, L.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Hutter, L

Reference 20

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AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Automated data-driven modeling of building energy systems via machine learning algo- rithms,

Reference 21

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Observation 9e027cc4-553f-4460-a949-825d811498ca · outbound

This paper cites AutoAI-TS: AutoAI for time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability AutoAI-TS: AutoAI for time series forecasting,

Reference 22

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Observation 691b4f4f-2a58-45e0-a9a8-5d2d69dd0190 · outbound

This paper cites Efficient automated deep learning for time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Efficient automated deep learning for time series forecasting,

Reference 23

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Observation 615d183c-5289-45d9-a953-e6d642a4b94a · outbound

This paper cites AutoGluon–TimeSeries: AutoML for probabilistic time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability AutoGluon–TimeSeries: AutoML for probabilistic time series forecasting,

Reference 24

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Observation d39bdfe1-63f5-4416-86ea-7f7a2d53d6bf · outbound

This paper cites Reporting electricity consumption is essen- tial for sustainable AI,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Reporting electricity consumption is essen- tial for sustainable AI,

Reference 25

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Observation ecc4ddab-2d94-4229-ba8d-5f00bdde3fbf · outbound

This paper cites DeepAR: Probabilistic forecast- ing with autoregressive recurrent networks,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability DeepAR: Probabilistic forecast- ing with autoregressive recurrent networks,

Reference 26

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AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability End-to- end learning of coherent probabilistic forecasts for hierarchical time series,

Reference 27

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Observation 3d7445ca-d43e-4341-a165-d9a2bda01e85 · outbound

This paper cites Controlling non-stationarity and periodicities in time series generation using condi- tional invertible neural networks,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Controlling non-stationarity and periodicities in time series generation using condi- tional invertible neural networks,

Reference 28

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Observation 42134d59-0368-4c3f-ad37-d9111a5ea5b6 · outbound

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AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Quantile regression neural networks: Implementation in R and application to precipitation downscaling,

Reference 29

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This paper cites Probabilistic energy forecasting us- ing the nearest neighbors quantile filter and quantile regression,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Probabilistic energy forecasting us- ing the nearest neighbors quantile filter and quantile regression,

Reference 30

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correction dated 2021-05-19. Source: crossref record 10.1016/j.ijforecast.2021.01.011->10.1016/j.ijforecast.2019.06.003:correction, observed 2026-07-11T03:13:48.751494+00:00. This notice travels one citation hop only.

Observation 51308d94-0a34-4a50-9320-4c81c4570ea3 · outbound

This paper cites Deep generative quantile- copula models for probabilistic forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Deep generative quantile- copula models for probabilistic forecasting,

Reference 31

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Observation 83a7ba25-d5fc-45b2-8727-66b6f3f969d6 · outbound

This paper cites Multivariate proba- bilistic time series forecasting via conditioned nor- malizing flows,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Multivariate proba- bilistic time series forecasting via conditioned nor- malizing flows,

Reference 32

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Observation 1fd79a50-e885-43ca-b1e0-1e2b30bed08e · outbound

This paper cites Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand

Reference 33

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Observation 4129398d-0437-4e5f-a140-5fc6af2148ed · outbound

This paper cites Short-term density forecasting of low-voltage load using Bernstein-polynomial nor- malizing flows,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Short-term density forecasting of low-voltage load using Bernstein-polynomial nor- malizing flows,

Reference 34

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Observation b0d338e5-6b3f-48dc-beca-20dd7e6a846d · outbound

This paper cites Probabilistic forecasting using deep gen- erative models,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Probabilistic forecasting using deep gen- erative models,

Reference 35

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Observation ee6ba4fa-63a2-4f58-8519-f2c5e9c2ecd3 · outbound

This paper cites Scenario forecasting of residential load profiles,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Scenario forecasting of residential load profiles,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:57.005938Z

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Observation f7f983bc-7838-4ce6-bedf-5222e4592ab5 · outbound

This paper cites Modeling daily load profiles of distribu- tion network for scenario generation using flow- based generative network,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Modeling daily load profiles of distribu- tion network for scenario generation using flow- based generative network,

Reference 37

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This paper cites A deep generative model for probabilistic energy forecasting in power systems: Normalizing flows,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability A deep generative model for probabilistic energy forecasting in power systems: Normalizing flows,

Reference 38

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Observation 15f3d2b4-9e8f-40af-ab8f-e5c9e0539016 · outbound

This paper cites Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows,

Reference 39

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Observation 2f91560e-e501-4afe-b0d8-ef3734d62ecd · outbound

This paper cites Distri- butional conformal prediction,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Distri- butional conformal prediction,

Reference 40

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Observation d3ea6660-0db9-454b-850f-be37739d4ddc · outbound

This paper cites Adaptive conformal predictions for time series,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Adaptive conformal predictions for time series,

Reference 41

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Observation fa11ed67-0319-4fb0-937d-eb904fff934c · outbound

This paper cites A state space framework for au- tomatic forecasting using exponential smoothing methods,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability A state space framework for au- tomatic forecasting using exponential smoothing methods,

Reference 42

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Observation 997a030a-3959-4d96-b120-4068e188cfdd · outbound

This paper cites Automatic time series forecasting: The forecast package for R,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Automatic time series forecasting: The forecast package for R,

Reference 43

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Observation acd937d2-5d60-47c6-b9a6-c57601ddfd19 · outbound

This paper cites Forecasting time series with complex seasonal pat- terns using exponential smoothing,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Forecasting time series with complex seasonal pat- terns using exponential smoothing,

Reference 44

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Observation 187620b6-aa9a-4416-a178-f6d9ea4b6299 · outbound

This paper cites Au- tomatic time series analysis for electric load fore- casting via support vector regression,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Au- tomatic time series analysis for electric load fore- casting via support vector regression,

Reference 45

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Observation e512766c-14d9-47fc-8555-7bf4c4d80cde · outbound

This paper cites SVR-FFS: A novel forward feature selection approach for high- frequency time series forecasting using support vec- tor regression,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability SVR-FFS: A novel forward feature selection approach for high- frequency time series forecasting using support vec- tor regression,

Reference 46

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Observation c0a3ce4a-1b4a-4eb6-b5a6-b738f6ea9d08 · outbound

This paper cites ELM- based improved layered ensemble architecture for time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability ELM- based improved layered ensemble architecture for time series forecasting,

Reference 47

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Observation 9321b090-35d6-4dae-aa25-fa0679c094d1 · outbound

This paper cites Em- pirical study on the impact of different sets of pa- rameters of gradient boosting algorithms for time- series forecasting with LightGBM,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Em- pirical study on the impact of different sets of pa- rameters of gradient boosting algorithms for time- series forecasting with LightGBM,

Reference 48

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Observation 88d78773-4048-4395-ab2b-dc739c02b2d7 · outbound

This paper cites AutoCTS: Automated correlated time series forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability AutoCTS: Automated correlated time series forecasting,

Reference 49

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Observation 9b9b9873-db1f-4e27-81da-3b4fb1c804d9 · outbound

This paper cites Effect of automatic hyperparame- ter tuning for residential load forecasting via deep learning,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Effect of automatic hyperparame- ter tuning for residential load forecasting via deep learning,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:53.873482Z

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Observation af6cba96-7f68-4c48-8adc-9e45edc6ccbd · outbound

This paper cites A hybrid deep learning model with evolutionary al- gorithm for short-term load forecasting,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability A hybrid deep learning model with evolutionary al- gorithm for short-term load forecasting,

Reference 51

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Observation 46fc261c-9d45-4fd8-b9cb-f8847843f13f · outbound

This paper cites an unresolved cited work.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 52

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Observation 468cd3bd-c184-4c82-8c8b-03ab0e55ea71 · outbound

This paper cites Review of automated time series forecasting pipelines,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Review of automated time series forecasting pipelines,

Reference 53

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Observation 1ded95e0-056d-40d3-8df7-7ff4788fa2f5 · outbound

This paper cites Random search for hy- perparameter optimization,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Random search for hy- perparameter optimization,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.894838Z

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

Observation 20db4fa1-70bb-4327-8472-088468b27f5e · outbound

This paper cites Ardizzone, C.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Ardizzone, C

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.875297Z

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 61607cf5-8e87-4d6e-9634-a47fb6a203b8 · outbound

This paper cites Forecasting energy time series with profile neural networks,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Forecasting energy time series with profile neural networks,

Reference 56

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T05:30:54.779019Z

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 b1d19e26-eb59-4738-91ca-2956daaf1f61 · outbound

This paper cites Short-term electricity load forecasting using the Temporal Fu- sion Transformer: Effect of grid hierarchies and data sources,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Short-term electricity load forecasting using the Temporal Fu- sion Transformer: Effect of grid hierarchies and data sources,

Reference 57

Resolution: verified exact; doi, observed 2026-08-12T05:30:54.082748Z

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 7e98c604-271b-4b63-93b7-59bf509860c0 · outbound

This paper cites Day-ahead electricity load prediction based on calendar features and temporal convolutional networks,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Day-ahead electricity load prediction based on calendar features and temporal convolutional networks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.857642Z

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 97266221-d6c2-4c65-a5a8-ccc1a11d0495 · outbound

This paper cites Loss- customised probabilistic energy time series fore- casts using automated hyperparameter optimisation,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Loss- customised probabilistic energy time series fore- casts using automated hyperparameter optimisation,

Reference 59

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T05:30:54.693639Z

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 e34f759d-9228-4e9f-a8a1-9d84b91a9c2f · outbound

This paper cites Making a sci- ence of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Making a sci- ence of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.839400Z

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 772bd251-e4bd-446a-9cba-30f34098bb5e · outbound

This paper cites Massively parallel genetic op- timization through asynchronous propagation of populations,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Massively parallel genetic op- timization through asynchronous propagation of populations,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.819191Z

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 727bc558-639f-4e25-bfed-4f0a314f9585 · outbound

This paper cites Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.798439Z

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 dc515033-c4b6-48f5-9c0c-180d2e5128ba · outbound

This paper cites Open Power System Data: Friction- less data for electricity system modelling,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Open Power System Data: Friction- less data for electricity system modelling,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.779261Z

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 037099a0-f362-4103-ab2f-ae3f9e594bf2 · outbound

This paper cites Trindade, Electricity load diagrams 2011-2014, UCI Machine Learning Repository, 2015.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Trindade, Electricity load diagrams 2011-2014, UCI Machine Learning Repository, 2015

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.729711Z

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 f922cac2-92c0-498f-8a6c-a12b54e8d866 · outbound

This paper cites Fanaee-T, Bike sharing dataset, UCI Machine Learning Repository, 2013.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Fanaee-T, Bike sharing dataset, UCI Machine Learning Repository, 2013

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.712174Z

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 c5553d37-0d75-409a-8944-6b10555f2d1d · outbound

This paper cites Probabilistic energy fore- casting: Global energy forecasting competition 2014 and beyond,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Probabilistic energy fore- casting: Global energy forecasting competition 2014 and beyond,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:53.972624Z

Source-reported events for the cited work

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Observation 232d461b-8e89-4ac8-a94d-80027b97c6b8 · outbound

This paper cites Prob- abilistic forecasts, calibration and sharpness,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Prob- abilistic forecasts, calibration and sharpness,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:53.977556Z

Source-reported events for the cited work

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Observation 7af74aad-414a-4bb2-aabd-c607b3beb6df · outbound

This paper cites PyTorch Forecasting.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability PyTorch Forecasting

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:53.983120Z

Source-reported events for the cited work

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Observation 7abb69db-24a4-44b6-9b57-b23b979926b2 · outbound

This paper cites 1016 / j.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability 1016 / j

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.762310Z

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 065df5ef-a356-4eca-b9dd-3accfb6fa03a · outbound

This paper cites Scikit-learn: Machine learning in Python,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Scikit-learn: Machine learning in Python,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.657615Z

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 53ca016b-af80-4bb8-9257-33e7f295678d · outbound

This paper cites Amazon EC2 G4 in- stances.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Amazon EC2 G4 in- stances

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.635004Z

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 8225b295-4892-41fe-9419-e9097494dce9 · outbound

This paper cites Open Access uptake by universities worldwide.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Open Access uptake by universities worldwide

Reference 72

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T05:30:54.526874Z

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 3bb2a2fa-0547-4ec9-80b4-89c038040efc · outbound

This paper cites The impact of fore- cast characteristics on the forecast value for the dispatchable feeder,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability The impact of fore- cast characteristics on the forecast value for the dispatchable feeder,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:54.009656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e3a6a37-289d-4ac1-9d07-7798f427b16d · outbound

This paper cites sktime: A unified inter- face for machine learning with time series,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability sktime: A unified inter- face for machine learning with time series,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.614823Z

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 aeac249c-6b95-4763-a90f-159db777822b · outbound

This paper cites Multi-step-ahead time series prediction using multiple-output support vec- tor regression,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Multi-step-ahead time series prediction using multiple-output support vec- tor regression,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.595564Z

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 897d7b93-85fe-45d6-96bf-a3b93659f83c · outbound

This paper cites Chollet et al., Keras, https://keras.io, 2015.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Chollet et al., Keras, https://keras.io, 2015

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.679101Z

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 47e5d50a-e4d4-4492-a29f-a2bd02e327ba · outbound

This paper cites XGBoost: A scalable tree boosting system,.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability XGBoost: A scalable tree boosting system,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:54.025074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9413bf90-c502-4481-9c64-03af5e0f4d87 · outbound

This paper cites Available: https://open-power- system-data.org/.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Available: https://open-power- system-data.org/

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:56.746506Z

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 53d66006-57e8-483d-b66b-d72c8002c1c2 · outbound

This paper cites an unresolved cited work.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 2019

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T05:30:55.957281Z

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 e651abcb-cc95-4f84-8f23-c71bac26bf80 · outbound

This paper cites an unresolved cited work.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 2020

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T05:30:55.651861Z

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 aa288ac4-8279-4570-af4e-03b26c6c9ff2 · outbound

This paper cites an unresolved cited work.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T05:30:53.893501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78a0d66e-0669-4c71-a82f-a204c62757a8 · outbound

This paper cites 1016 / j.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability 1016 / j

Reference 2619

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:30:57.266509Z

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 239583a5-dbee-4b60-9ba1-3e4f37e4c854 · outbound

This paper cites an unresolved cited work.

AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability Unresolved cited work

Reference 3417

Resolution: verified exact; doi, observed 2026-08-12T05:30:54.237640Z

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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Reference resolution

Outbound references

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