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Paper Citation Record · LEDGER

Deep Learning Network-Temporal Models For Traffic Prediction

As of 19 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2603.11475.

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

pith.paper-citation-record.v1
2603.11475 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T22:51:55.339022Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8158b82-a51f-4cf6-bb9f-3e231cab8bf9 · outbound

This paper cites Network planning with deep reinforcement learning,.

Deep Learning Network-Temporal Models For Traffic Prediction Network planning with deep reinforcement learning,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:117d0e0d7b98c1dc2189c0711cbf194c717efabf62b4bb93f8afc5193d22f324

Observation 47f080df-4992-4a31-8e4a-dbdaccf090c5 · outbound

This paper cites Breaking boundaries: Balancing performance and robustness in deep wireless traffic fore- casting,.

Deep Learning Network-Temporal Models For Traffic Prediction Breaking boundaries: Balancing performance and robustness in deep wireless traffic fore- casting,

Reference 2

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:761234bac78a3272bf900a42766d1d11e53fb36093ce450d0f95365dc34c7f32

Observation c51acda6-28c3-4568-a9ef-0fedfb7a30fb · outbound

This paper cites Traffic engineering: from isp to cloud wide area networks,.

Deep Learning Network-Temporal Models For Traffic Prediction Traffic engineering: from isp to cloud wide area networks,

Reference 3

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Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:ee4d00c726b63d668eeb124a07f6132c485722ed3936a04fb7214882e042fd77

Observation 5437ffc5-6e6a-4b1a-b5e2-bde6faa140ed · outbound

This paper cites Proactive and aoi-aware failure recovery for stateful nfv-enabled zero-touch 6g networks: Model-free drl approach,.

Deep Learning Network-Temporal Models For Traffic Prediction Proactive and aoi-aware failure recovery for stateful nfv-enabled zero-touch 6g networks: Model-free drl approach,

Reference 4

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:0babd2ce6396ec95ea3a0c50da026c1d36952efad4184ba3c60376297257023b

Observation 2bc1661e-0a20-48a8-b827-7510032ad697 · outbound

This paper cites Monitor- ing and diagnostic technologies using deep neural networks for predic- tive optical network maintenance,.

Deep Learning Network-Temporal Models For Traffic Prediction Monitor- ing and diagnostic technologies using deep neural networks for predic- tive optical network maintenance,

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:a97c0650d0efee86d2e6c612e5370115c28f18a00c2b0cd48bb3f1a94241a18e

Observation e7fa05a2-48d3-46d0-b312-ec46611b62a9 · outbound

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

Deep Learning Network-Temporal Models For Traffic Prediction Modeling long-and short-term temporal patterns with deep neural networks,

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:268e8ceee00f8afcc510478aa976a967ef7137ca22609f7977001964f08cb06a

Observation 4fe25156-feb8-4f21-8e54-9743f97dd8f6 · outbound

This paper cites Forecast evaluation for data scientists: common pitfalls and best practices,.

Deep Learning Network-Temporal Models For Traffic Prediction Forecast evaluation for data scientists: common pitfalls and best practices,

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:3d0e6329b8298d4d4ec68f0dae2321bc80ba7bd20270d4fb814fbe195e7e513a

Observation a46f2cd0-e96b-4aa1-9602-5a76c5afc809 · outbound

This paper cites A resource-aware multi-graph neural network for urban traffic flow prediction in multi-access edge computing systems,.

Deep Learning Network-Temporal Models For Traffic Prediction A resource-aware multi-graph neural network for urban traffic flow prediction in multi-access edge computing systems,

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:969c05ae051b18022910ab7df56868663ebb216cba5841dead0447ef4aadd3a3

Observation 795fff5a-ca9d-41ad-8fcb-44d330a3f222 · outbound

This paper cites Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting,.

Deep Learning Network-Temporal Models For Traffic Prediction Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting,

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:a2ebf85070d6ce4e2085ca4c3b1545df8efeeee1dd0713025bd1a7012aaa4075

Observation 567dffce-f9ab-423a-92fe-e174db1097cf · outbound

This paper cites Predicting wan traffic volumes using fourier and multivariate sarima approach,.

Deep Learning Network-Temporal Models For Traffic Prediction Predicting wan traffic volumes using fourier and multivariate sarima approach,

Reference 10

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:c4d6b7338debe56d266f412c9bfa35b859b01652e8e2b0be25133e40ad0212d1

Observation f6609867-602c-43d1-b628-f2c35425d329 · outbound

This paper cites TimeGPT-1.

Deep Learning Network-Temporal Models For Traffic Prediction TimeGPT-1

Reference 11

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:e31b4d63acca674aef2efea8c32064323e0d7397a370bb303f4e2c0098092595

Observation 81c5e761-3798-49a4-830e-943f580ac867 · outbound

This paper cites Large language models are zero-shot time series forecasters,.

Deep Learning Network-Temporal Models For Traffic Prediction Large language models are zero-shot time series forecasters,

Reference 12

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source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:35a4f6f24f0a0bbb164e21326e37c720f57e748dbe6f1ccb3aab5a5a3395c63d

Observation 8358963e-45d8-4407-917d-f50a00ab0747 · outbound

This paper cites Time-llm: Time series forecasting by reprogramming large language models,.

Deep Learning Network-Temporal Models For Traffic Prediction Time-llm: Time series forecasting by reprogramming large language models,

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:77127fbf62d696f9e4f5a9d6082bd14d326056718e4073aa668d8cad3ab77233

Observation 6bf874fd-e450-4aae-a198-02e53f503403 · outbound

This paper cites Calf: Aligning llms for time series forecasting via cross- modal fine-tuning,.

Deep Learning Network-Temporal Models For Traffic Prediction Calf: Aligning llms for time series forecasting via cross- modal fine-tuning,

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T22:51:55.339022Z digest=sha256:6294006b6afcf36fefd0c7da05d284d478c2fccb109e97cae84e255761143064

Pith citing papers

No inbound Pith citation observations are available.