Paper Citation Record · LEDGER

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2501.13794.

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

pith.paper-citation-record.v1
2501.13794 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:40:30.035161Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T07:35:27.894693Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T07:36:41.882622Z

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy26
unresolved11
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4abc42eb-9fdb-48a6-944b-e0acd56edbcd · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Adaptive graph convolutional recurrent network for traffic forecasting

Reference 1

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Source-reported events for the cited work

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Observation e3f0f590-d9c9-40d4-bc76-e396801274ee · outbound

This paper cites • ARIMA: The ARIMA model is a frequently applied sta- tistical approach for time series forecasting.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction • ARIMA: The ARIMA model is a frequently applied sta- tistical approach for time series forecasting

Reference 2

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Source-reported events for the cited work

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Observation da95e890-be1a-4f9c-95a9-6be7c4597158 · outbound

This paper cites Pre- serve your own correlation: A noise prior for video dif- fusion models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Pre- serve your own correlation: A noise prior for video dif- fusion models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.500877Z

Source-reported events for the cited work

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Observation 40db32b9-424c-4c77-b7af-163d90a33cd1 · outbound

This paper cites Denoising diffusion probabilistic models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Denoising diffusion probabilistic models

Reference 7

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Observation 99e2fc20-2c7e-4f2d-9f14-18300088534b · outbound

This paper cites Video diffusion models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Video diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.484882Z

Source-reported events for the cited work

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Observation d022308a-b948-4d9e-a882-f142261edd91 · outbound

This paper cites Attentive crowd flow machines.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Attentive crowd flow machines

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.453814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c49ee736-61e2-4bb8-b8f3-0d18124775ca · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T15:40:29.943815Z

Source-reported events for the cited work

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Observation 0a0c5947-f731-47cc-b0d5-e1e57bb51765 · outbound

This paper cites Improved denoising diffusion proba- bilistic models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Improved denoising diffusion proba- bilistic models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.443682Z

Source-reported events for the cited work

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Observation 4e352e35-b165-4da4-aa64-f2fe7a8d2ead · outbound

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

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 14

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Source-reported events for the cited work

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Observation 0648852b-9809-47ac-a68d-749defa3120c · outbound

This paper cites FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T15:40:29.954419Z

Source-reported events for the cited work

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Observation b36b7b4e-dda0-460c-a454-c4d9adbb488c · outbound

This paper cites Mo- bile data science and intelligent apps: concepts, ai-based modeling and research directions.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Mo- bile data science and intelligent apps: concepts, ai-based modeling and research directions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.433171Z

Source-reported events for the cited work

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Observation 66276585-75da-41b3-a977-e936a03d149f · outbound

This paper cites Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing

Reference 17

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Source-reported events for the cited work

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Observation 8a738e53-ec93-4022-a293-ae433ae6b7e7 · outbound

This paper cites Non- autoregressive conditional diffusion models for time se- ries prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Non- autoregressive conditional diffusion models for time se- ries prediction

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.416794Z

Source-reported events for the cited work

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Observation 32afd357-4c89-4eee-aea9-1d50aa2deb6b · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Convolutional lstm network: A machine learning approach for precipitation nowcasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.405712Z

Source-reported events for the cited work

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Observation 302a92a4-2ec9-4150-ab96-9d898acfa36b · outbound

This paper cites Csdi: Conditional score- based diffusion models for probabilistic time series impu- tation.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Csdi: Conditional score- based diffusion models for probabilistic time series impu- tation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.385216Z

Source-reported events for the cited work

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Observation 965be915-b9d8-4aa3-8e44-dfb9fa8e7432 · outbound

This paper cites Predrnn: Recurrent neural networks for predictive learning using spatiotem- poral lstms.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Predrnn: Recurrent neural networks for predictive learning using spatiotem- poral lstms

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.375358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 653a858b-9037-4980-9835-550c82b6c7ca · outbound

This paper cites A survey on video diffusion models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction A survey on video diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.349527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7b49fda2-7068-4755-a1df-435f83aea415 · outbound

This paper cites Network traffic overload prediction with tempo- ral graph attention convolutional networks.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Network traffic overload prediction with tempo- ral graph attention convolutional networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.330412Z

Source-reported events for the cited work

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Observation 89e72525-5133-4fca-a732-bf93fbc6b99b · outbound

This paper cites Spatio-temporal diffu- sion point processes.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Spatio-temporal diffu- sion point processes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.320193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9ee62580-dfdf-4251-8b37-aeea458fa148 · outbound

This paper cites UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction

Reference 29

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Source-reported events for the cited work

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Observation cdb9c707-c425-4e0b-83e8-800670c21e2c · outbound

This paper cites Urbandit: A founda- tion model for open-world urban spatio-temporal learning.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Urbandit: A founda- tion model for open-world urban spatio-temporal learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T15:40:30.004209Z

Source-reported events for the cited work

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Observation fdc08a54-6885-4dec-90ee-e393665e1435 · outbound

This paper cites Deep spatio-temporal residual networks for citywide crowd flows prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Deep spatio-temporal residual networks for citywide crowd flows prediction

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.309245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6d6eb185-7251-4cd4-8e6b-34bb053c9159 · outbound

This paper cites Promptst: Prompt- enhanced spatio-temporal multi-attribute prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Promptst: Prompt- enhanced spatio-temporal multi-attribute prediction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.299241Z

Source-reported events for the cited work

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Observation 9adc69dd-6074-449b-8f22-ffe130ec8636 · outbound

This paper cites Trip: Temporal residual learning with image noise prior for image-to-video diffusion models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Trip: Temporal residual learning with image noise prior for image-to-video diffusion models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.288922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be14e3c7-42e1-4418-9a4d-6d8afc7cbc02 · outbound

This paper cites T-gcn: A temporal graph convolutional network for traffic prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction T-gcn: A temporal graph convolutional network for traffic prediction

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.277797Z

Source-reported events for the cited work

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Observation bc26ffaf-bcc2-4f35-afc7-77ca0d6e7649 · outbound

This paper cites St-gsp: Spatial-temporal global semantic represen- tation learning for urban flow prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction St-gsp: Spatial-temporal global semantic represen- tation learning for urban flow prediction

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.267780Z

Source-reported events for the cited work

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Observation 7029dc46-d9a5-4b03-9852-e1e72800b0b7 · outbound

This paper cites Towards generative modeling of urban flow through knowledge-enhanced denoising diffu- sion.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Towards generative modeling of urban flow through knowledge-enhanced denoising diffu- sion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.257680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 62c5d2cf-8e7c-4330-9f56-015f755a2b3f · outbound

This paper cites ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution

Reference 37

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Observation f6be497f-527a-4ae9-8fe3-978d71371809 · outbound

This paper cites We use a batch size of 8 and an initial learning rate of 1e-3, which is reduced to 4e-4 after 40 epochs.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction We use a batch size of 8 and an initial learning rate of 1e-3, which is reduced to 4e-4 after 40 epochs

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.247538Z

Source-reported events for the cited work

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Observation dab1d823-3afb-49de-87ef-d855df8d52c5 · outbound

This paper cites Temporal attention unit: Towards efficient spatiotemporal predictive learning.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Temporal attention unit: Towards efficient spatiotemporal predictive learning

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.395614Z

Source-reported events for the cited work

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Observation c656b4c4-d653-47fc-93e6-2f942754672a · outbound

This paper cites Get rid of isolation: A continuous multi-task spatio- temporal learning framework.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Get rid of isolation: A continuous multi-task spatio- temporal learning framework

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.340409Z

Source-reported events for the cited work

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Observation d562bc5a-923f-4794-acb9-132b13513e1b · outbound

This paper cites Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotempo- ral dynamics.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotempo- ral dynamics

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.365195Z

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Observation 50779198-0db9-453f-8da8-7dba592e732c · outbound

This paper cites Self-attention con- vlstm for spatiotemporal prediction.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Self-attention con- vlstm for spatiotemporal prediction

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.463909Z

Source-reported events for the cited work

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Observation b736d113-2dd5-4bce-b286-4599bd8faa80 · outbound

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

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models

Reference 2019

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Source-reported events for the cited work

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Observation 9f69c901-7cd0-41cf-8f0c-d081a691ea28 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2020

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Source-reported events for the cited work

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Observation 940793a4-c5b0-4371-b891-d59a38f3e3da · outbound

This paper cites How i warped your noise: a temporally-correlated noise prior for diffusion models.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction How i warped your noise: a temporally-correlated noise prior for diffusion models

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.517374Z

Source-reported events for the cited work

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Observation f4c90957-f933-4263-b58b-94cf456706c4 · outbound

This paper cites Time-llm: Time series forecasting by reprogramming large language mod- els.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Time-llm: Time series forecasting by reprogramming large language mod- els

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.474436Z

Source-reported events for the cited work

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Observation 66e4bb1d-9a52-419d-84e9-82dfb5cdad07 · outbound

This paper cites Mau: A motion-aware unit for video prediction and be- yond.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction Mau: A motion-aware unit for video prediction and be- yond

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:40:30.527918Z

Source-reported events for the cited work

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Observation 8955ff19-86d7-4b06-80f6-a3f6b77a7c4e · outbound

This paper cites St-norm: Spatial and tem- poral normalization for multi-variate time series forecast- ing.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction St-norm: Spatial and tem- poral normalization for multi-variate time series forecast- ing

Reference 2024

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Source-reported events for the cited work

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Pith citing papers

Observation 4aedaab5-aadb-4861-b1d7-4f0e5e74fe01 · inbound

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction cites this paper.

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:20:42.157820Z

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Observation f384ff6f-94f6-433e-b6d9-7fca6557f989 · inbound

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction cites this paper.

STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:36:41.885594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.