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Paper Citation Record · LEDGER

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.23253.

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

pith.paper-citation-record.v1
2507.23253 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:58:02.575921Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 623eb0d0-c070-4c03-a4e6-a19cdaba3374 · outbound

This paper cites an unresolved cited work.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 2

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Observation 7d40801b-a420-4645-b769-3d40f1106ce2 · outbound

This paper cites VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Reference 3

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This paper cites Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting

Reference 4

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This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 5

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 6

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 7

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 8

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 9

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 10

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 11

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 12

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 13

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 14

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

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Reference 16

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Observation 332bf5c7-c797-4af9-8917-fac6319527ee · outbound

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 17

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Observation 8d671638-8a27-42db-84f8-27fbd7e97365 · outbound

This paper cites TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

Reference 18

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Reference 19

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Reference 20

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Reference 21

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality An Introduction to Autoencoders

Reference 22

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

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 23

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

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 24

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Reference 25

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

Reference 27

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality LightGTS: A Lightweight General Time Series Forecasting Model

Reference 28

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Reference 29

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality A Multi-Horizon Quantile Recurrent Forecaster

Reference 31

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Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 32

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Reference 33

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Source-reported events for the cited work

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Reference 34

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Source-reported events for the cited work

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raw_fallback, observed 2026-08-06T10:58:02.677309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T10:58:02.568907Z digest=sha256:7c38983ff5885c5b77df9056da696647ea8e93cac53b70c84ef68eaa84ed4113

Observation dcd4f4ef-c6c2-4762-8d29-9caaea2b9406 · outbound

This paper cites an unresolved cited work.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-06T10:58:02.910236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T10:58:02.572369Z digest=sha256:7fc721bf46cc6b8a85b6efc8f929bd883eeb0fed9766f98ebba6bea82ed85ed3

Observation 634267ef-16c4-49aa-af65-b3b2dd9ffc3c · outbound

This paper cites an unresolved cited work.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-06T10:58:02.899718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T10:58:02.575921Z digest=sha256:8db16473c072d932c3e53b4b5eade5e273ba8ad72708b1432bc3f0aeba6a1982

Observation dc1a8d71-1d34-4875-b6e9-aded69e75482 · outbound

This paper cites an unresolved cited work.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality Unresolved cited work

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T10:58:02.565282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:58:02.565282Z digest=sha256:5720d661fb89d03f3c1dc5d071de11700ddf3815e907bddebd86ef5006362086

Pith citing papers

No inbound Pith citation observations are available.