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Paper Citation Record · LEDGER

Non-collective Calibrating Strategy for Time Series Forecasting

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.03176.

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

pith.paper-citation-record.v1
2506.03176 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:52:51.106721Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

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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

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation c934759a-2610-423d-a759-ddf16dcbd5ef · outbound

This paper cites The First World Sci- ence and Intelligence Competition: Atmospheric Science Track - East China AI Medium-Range Weather Forecast- ing Competition.

Non-collective Calibrating Strategy for Time Series Forecasting The First World Sci- ence and Intelligence Competition: Atmospheric Science Track - East China AI Medium-Range Weather Forecast- ing Competition

Reference 1

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Observation 3902fc8a-7342-499b-a49b-c3a78a26f122 · outbound

This paper cites Conditional time series fore- casting with convolutional neural networks.

Non-collective Calibrating Strategy for Time Series Forecasting Conditional time series fore- casting with convolutional neural networks

Reference 5

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This paper cites Arik, Nathanael C.

Non-collective Calibrating Strategy for Time Series Forecasting Arik, Nathanael C

Reference 6

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Observation ff16a349-9c40-494d-b73c-a4d1b0879597 · outbound

This paper cites A multi-view multi-task learning framework for multi-variate time series forecast- ing.

Non-collective Calibrating Strategy for Time Series Forecasting A multi-view multi-task learning framework for multi-variate time series forecast- ing

Reference 7

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Observation 34b4abc3-1d70-4045-b776-e68b6f83016e · outbound

This paper cites SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion.

Non-collective Calibrating Strategy for Time Series Forecasting SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

Reference 8

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Observation e92cc46d-e0b0-4433-b9b3-f6d1f928c645 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Non-collective Calibrating Strategy for Time Series Forecasting Gaussian Error Linear Units (GELUs)

Reference 9

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Observation 7d664cd6-feb2-47f0-9ee2-b749e400c0c1 · outbound

This paper cites Prediction then cor- rection: An abductive prediction correction method for se- quential recommendation.

Non-collective Calibrating Strategy for Time Series Forecasting Prediction then cor- rection: An abductive prediction correction method for se- quential recommendation

Reference 10

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Observation f3340f83-7421-4c7e-aadc-1b4ec77860a0 · outbound

This paper cites Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen.

Non-collective Calibrating Strategy for Time Series Forecasting Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen

Reference 11

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Observation ea98ffbf-0e30-4e0a-8444-e96dadd1c2d5 · outbound

This paper cites Accurate uncertainties for deep learning using calibrated regression.

Non-collective Calibrating Strategy for Time Series Forecasting Accurate uncertainties for deep learning using calibrated regression

Reference 12

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This paper cites Evaluation of near- surface wind speed climatology and long-term trend over china’s mainland region based on ERA5 reanalysis.

Non-collective Calibrating Strategy for Time Series Forecasting Evaluation of near- surface wind speed climatology and long-term trend over china’s mainland region based on ERA5 reanalysis

Reference 14

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Observation ceee98cb-4b41-4c1e-8d98-89ad499db274 · outbound

This paper cites Unitime: A language-empowered unified model for cross-domain time series forecasting.

Non-collective Calibrating Strategy for Time Series Forecasting Unitime: A language-empowered unified model for cross-domain time series forecasting

Reference 16

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Observation 239c9fbc-c89b-45e5-8891-42632cf17e3f · outbound

This paper cites Structural property-aware multilayer net- work embedding for latent factor analysis.

Non-collective Calibrating Strategy for Time Series Forecasting Structural property-aware multilayer net- work embedding for latent factor analysis

Reference 17

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Observation ad27b0d6-9da5-4ac4-8e69-4d4ab643adc4 · outbound

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Non-collective Calibrating Strategy for Time Series Forecasting Fuzzy multiple-source transfer learning

Reference 18

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Observation d2208ca3-71cf-48ea-8090-1e2c07e6ef8f · outbound

This paper cites Probabilistic outputs for support vector machines and comparisons to regular- ized likelihood methods.

Non-collective Calibrating Strategy for Time Series Forecasting Probabilistic outputs for support vector machines and comparisons to regular- ized likelihood methods

Reference 20

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Observation 7b1fd005-1a2c-4912-8eb2-38f2cbb4961d · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation.

Non-collective Calibrating Strategy for Time Series Forecasting U-Net: Convolutional networks for biomedical image segmentation

Reference 23

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Observation 1a0605dc-195b-4742-a873-2c8b4971cf5c · outbound

This paper cites Are lan- guage models actually useful for time series forecasting? In NeurIPS,.

Non-collective Calibrating Strategy for Time Series Forecasting Are lan- guage models actually useful for time series forecasting? In NeurIPS,

Reference 25

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Observation 88d4d7cb-b379-4bd5-bb1e-46fbdb40db41 · outbound

This paper cites Deep Time Series Models: A Comprehensive Survey and Benchmark.

Non-collective Calibrating Strategy for Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark

Reference 26

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Observation 6537a698-3852-4d5f-95e6-839c89e147d1 · outbound

This paper cites Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models.

Non-collective Calibrating Strategy for Time Series Forecasting Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models

Reference 27

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This paper cites TimesNet: Temporal 2d-variation modeling for general time series analysis.

Non-collective Calibrating Strategy for Time Series Forecasting TimesNet: Temporal 2d-variation modeling for general time series analysis

Reference 28

Resolution
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This paper cites Are transformers effective for time series fore- casting? Proceedings of the AAAI Conference on Artificial Intelligence, 37(9):11121–11128, Jun.

Non-collective Calibrating Strategy for Time Series Forecasting Are transformers effective for time series fore- casting? Proceedings of the AAAI Conference on Artificial Intelligence, 37(9):11121–11128, Jun

Reference 29

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Non-collective Calibrating Strategy for Time Series Forecasting MAP-FCRNN: multi-step ahead prediction model using forecasting correction and RNN model with memory functions.Inf

Reference 30

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Non-collective Calibrating Strategy for Time Series Forecasting ProbTS: Benchmarking point and distributional forecasting across diverse prediction horizons

Reference 31

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This paper cites Time-vlm: Exploring multimodal vision-language models for aug- mented time series forecasting.

Non-collective Calibrating Strategy for Time Series Forecasting Time-vlm: Exploring multimodal vision-language models for aug- mented time series forecasting

Reference 32

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Reference 34

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Reference 35

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Reference 37

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Reference 720

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Reference 1999

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This paper cites Jensen, Zhenli Sheng, and Bin Yang.

Non-collective Calibrating Strategy for Time Series Forecasting Jensen, Zhenli Sheng, and Bin Yang

Reference 2000

Resolution
verified fuzzy
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Source-reported events for the cited work

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This paper cites Predict-then-Calibrate: A New Perspective of Robust Contextual LP.

Non-collective Calibrating Strategy for Time Series Forecasting Predict-then-Calibrate: A New Perspective of Robust Contextual LP

Reference 2015

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Non-collective Calibrating Strategy for Time Series Forecasting Classifier calibration with roc-regularized isotonic regression

Reference 2016

Resolution
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Observation ddc7d07b-3078-4dc8-9d29-3fcf3db7df1f · outbound

This paper cites Modeling long- and short-term temporal patterns with deep neural networks.

Non-collective Calibrating Strategy for Time Series Forecasting Modeling long- and short-term temporal patterns with deep neural networks

Reference 2018

Resolution
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Source-reported events for the cited work

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Observation 5bca9d4d-0659-4d39-9992-bb71ed640344 · outbound

This paper cites Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.

Non-collective Calibrating Strategy for Time Series Forecasting Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam

Reference 2019

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Source-reported events for the cited work

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Observation 12c53704-2fef-449d-b996-4fdf65f299ec · outbound

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Non-collective Calibrating Strategy for Time Series Forecasting Unresolved cited work

Reference 2020

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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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Observation 8194f0c9-4674-4798-9675-cd6817ab3a95 · outbound

This paper cites CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning.

Non-collective Calibrating Strategy for Time Series Forecasting CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T12:52:48.772973Z

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Unavailable: canonical work link unavailable.

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Observation 0051f348-f8d3-4ab8-abf4-056a18c50b8c · outbound

This paper cites Layer Normalization.

Non-collective Calibrating Strategy for Time Series Forecasting Layer Normalization

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T12:52:47.291131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:52:47.291131Z digest=sha256:dfd0fdb87de7a6645e7bf8d49b68001b1604783dca3132379031a15680400f8c

Observation ad5f2d99-7a2f-4a5e-b47b-8a7e66293d41 · outbound

This paper cites Accurate medium- range global weather forecasting with 3d neural networks.

Non-collective Calibrating Strategy for Time Series Forecasting Accurate medium- range global weather forecasting with 3d neural networks

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:52:58.494730Z

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.

source=pdf_text observed=2026-08-07T12:52:47.550287Z digest=sha256:2795a63e8bb1a218135ccd03ff30f714830010b7f030484db0d12dea24285456

Observation 041480a5-55f4-459b-befe-8eafc5246930 · outbound

This paper cites FEDformer: Fre- quency enhanced decomposed transformer for long-term series forecasting.

Non-collective Calibrating Strategy for Time Series Forecasting FEDformer: Fre- quency enhanced decomposed transformer for long-term series forecasting

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:52:52.393624Z

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.

source=pdf_text observed=2026-08-07T12:52:50.643131Z digest=sha256:f1b7c50f4c1ddbcc8595dcea260bb7f37ebbf5071ef29c3b68f80701f759cb28

Pith citing papers

No inbound Pith citation observations are available.