Paper Citation Record · LEDGER

T-Graphormer: Using Transformers for Spatiotemporal Forecasting

As of 12 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2501.13274.

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

pith.paper-citation-record.v1
2501.13274 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:21:57.513481Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:08:18.190212Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

56 of 56 outbound references displayed

verified exact0
verified fuzzy26
unresolved30
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff328c0e-1525-413a-a4b1-0c9da0954ba8 · outbound

This paper cites write newline.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting write newline

Reference 1

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Observation ef70626a-6cd1-4c56-a424-a58ecfbd6437 · outbound

This paper cites Diffusion-convolutional neural networks.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Diffusion-convolutional neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:58.030142Z

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Observation b2aafa75-e847-4952-ac77-cee8b949ddb1 · outbound

This paper cites Layer Normalization.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Layer Normalization

Reference 3

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Observation ddbe49f7-eb69-413b-b80b-30b2ed5864be · outbound

This paper cites Longformer: The Long-Document Transformer.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Longformer: The Long-Document Transformer

Reference 4

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Observation 2d8ae9f4-830e-4c71-bf14-3819efa64bed · outbound

This paper cites Probabilistic demand forecasting at scale.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Probabilistic demand forecasting at scale

Reference 5

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Source-reported events for the cited work

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Observation e740dcd7-de3e-40d7-8aeb-986da52b3ebb · outbound

This paper cites Time series analysis: forecasting and control.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Time series analysis: forecasting and control

Reference 6

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Observation f25b631e-acef-4da1-b18e-5ce513b0b318 · outbound

This paper cites Introduction to Time Series and Forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Introduction to Time Series and Forecasting

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:58.007182Z

Source-reported events for the cited work

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Observation ac035c67-f05b-49c0-80cf-84d964eff294 · outbound

This paper cites Language models are few-shot learners.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Language models are few-shot learners

Reference 8

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Source-reported events for the cited work

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Observation 2f1fb3bf-7433-4707-9454-68b1b36e7a15 · outbound

This paper cites Freeway performance measurement system: mining loop detector data.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Freeway performance measurement system: mining loop detector data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.992224Z

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Observation f0c5949c-fdb5-4549-a864-55d3f8f715e1 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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Observation e80f24fd-b6d3-426f-a841-a905bf14a658 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T16:21:57.389403Z

Source-reported events for the cited work

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Observation d2745d09-de1b-4005-bbe3-cde26450d4b3 · outbound

This paper cites A generalization of transformer networks to graphs.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting A generalization of transformer networks to graphs

Reference 12

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Source-reported events for the cited work

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Observation cbecfcd5-55ab-453c-9f16-e48ab01fdba5 · outbound

This paper cites Masked autoencoders as spatiotemporal learners.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Masked autoencoders as spatiotemporal learners

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.967136Z

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Observation 2a4673c6-63d3-4540-aa64-a4d1b8c44616 · outbound

This paper cites Long-Range Transformers for Dynamic Spatiotemporal Forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Long-Range Transformers for Dynamic Spatiotemporal Forecasting

Reference 14

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Observation 76ce8187-35a2-4aac-a07d-d8cb5c53d7ff · outbound

This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.958231Z

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Observation c4e38386-c215-4eec-88a4-d7c6db2361f9 · outbound

This paper cites Deep residual learning for image recognition.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Deep residual learning for image recognition

Reference 16

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Source-reported events for the cited work

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Observation 02d237df-3334-4d4c-8c38-34ddd74f6e3b · outbound

This paper cites Masked autoencoders are scalable vision learners.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Masked autoencoders are scalable vision learners

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.949106Z

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Observation cb2ac24c-9c5f-4c14-9e8d-541b3a50f040 · outbound

This paper cites Long short-term memory.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Long short-term memory

Reference 18

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Observation d343dffe-cdb1-4462-a987-c2db3c9dec2b · outbound

This paper cites Robust estimation of a location parameter.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Robust estimation of a location parameter

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.933739Z

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Observation a4369ca9-32eb-441b-9079-b2240cab1f79 · outbound

This paper cites Big data and its technical challenges.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Big data and its technical challenges

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.923860Z

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Observation c777f7f5-d018-4331-85ce-c650922972bc · outbound

This paper cites Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.914644Z

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Observation f131ad41-434c-4d63-838c-64d1797d7219 · outbound

This paper cites Scaling Laws for Neural Language Models.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Scaling Laws for Neural Language Models

Reference 22

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Observation bf0c5d0e-d61f-48c1-ac2b-d06bd376c8b8 · outbound

This paper cites Rethinking graph transformers with spectral attention.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Rethinking graph transformers with spectral attention

Reference 23

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Observation 03fe5869-1917-4bc1-bc1d-ad4a1e2a7bae · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Imagenet classification with deep convolutional neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.900066Z

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Observation 2057f0da-3a5e-4b09-b1ed-71055f50571d · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 25

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Observation 411882a7-bab9-4208-8fa7-48eb42a4166c · outbound

This paper cites Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.885183Z

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Observation e2a248a9-63d9-4cc0-84dc-d458872241a4 · outbound

This paper cites Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.874928Z

Source-reported events for the cited work

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Observation 1193cfd3-604c-4056-b3df-4ecea430d5f2 · outbound

This paper cites Largest: A benchmark dataset for large-scale traffic forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Largest: A benchmark dataset for large-scale traffic forecasting

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.865372Z

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Observation 34f05f38-8da3-40cf-bd66-a89f07df22af · outbound

This paper cites SGDR : Stochastic gradient descent with warm restarts.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting SGDR : Stochastic gradient descent with warm restarts

Reference 29

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Observation a3bba8c9-54b5-4ca7-950f-82beacdc5c28 · outbound

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting Decoupled weight decay regularization

Reference 30

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Observation cd1205ba-e0bb-4b1d-a67d-54c7ca54af80 · outbound

This paper cites Dynamic prediction of traffic volume through kalman filtering theory.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Dynamic prediction of traffic volume through kalman filtering theory

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.845613Z

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Observation 640b5ffb-de29-44d9-9f9c-44e07a269046 · outbound

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting WaveNet: A Generative Model for Raw Audio

Reference 32

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Observation 0040e897-b3df-45e0-933c-64fb7fc64497 · outbound

This paper cites Training language models to follow instructions with human feedback.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Training language models to follow instructions with human feedback

Reference 33

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting On the difficulty of training recurrent neural networks

Reference 34

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Observation 468aabc1-8057-41f6-bb3c-0b711d795007 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Pytorch: An imperative style, high-performance deep learning library

Reference 35

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Observation 8b5e8a97-76f1-462a-b12b-d311a97db43d · outbound

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting Improving language understanding by generative pre-training

Reference 36

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Observation a3ab651d-9d24-4ebf-bf0a-d22b8e373ba7 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 37

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This paper cites Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.805953Z

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Observation 8ea83dec-316b-43e4-85c2-aad862d00bca · outbound

This paper cites Discrete graph structure learning for forecasting multiple time series.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Discrete graph structure learning for forecasting multiple time series

Reference 39

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Observation cf4aa074-d7b9-4805-b6d8-f2fbbcfb4632 · outbound

This paper cites Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.790841Z

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Observation 997ce957-e556-4511-9d30-22835d84ca1d · outbound

This paper cites A tutorial on support vector regression.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting A tutorial on support vector regression

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.780755Z

Source-reported events for the cited work

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Observation bedf1afb-452a-4536-a07e-a1437250c6a2 · outbound

This paper cites Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.770518Z

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Observation 4c339193-32f8-4dff-bdc2-e6eee8953909 · outbound

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting Dropout: a simple way to prevent neural networks from overfitting

Reference 43

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Source-reported events for the cited work

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Observation 48334cf1-d0b3-47ba-a625-41c2b65d7077 · outbound

This paper cites Sequence to sequence learning with neural networks.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Sequence to sequence learning with neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.753940Z

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Observation 7799cfde-e17d-4a3b-aa85-83ba68b14449 · outbound

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting LLaMA: Open and Efficient Foundation Language Models

Reference 45

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T-Graphormer: Using Transformers for Spatiotemporal Forecasting Attention is all you need

Reference 46

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Source-reported events for the cited work

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Observation 05e26750-04d3-4e91-87c3-1568ccfe075e · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Graph wavenet for deep spatial-temporal graph modeling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.738609Z

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Observation fc510f81-1ca6-4e06-8c07-ed508b044d91 · outbound

This paper cites Connecting the dots: Multivariate time series forecasting with graph neural networks.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Connecting the dots: Multivariate time series forecasting with graph neural networks

Reference 48

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Observation 33aa71dc-9dcd-43f5-a763-25f3a6c69be2 · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34: 0 28877--28888, 2021.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34: 0 28877--28888, 2021

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.729378Z

Source-reported events for the cited work

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Observation 2204b55c-050d-4c0d-af9c-ab1906db4e81 · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.719355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 220cf271-3756-4da1-bc22-82d2e5558007 · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Multi-Scale Context Aggregation by Dilated Convolutions

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T16:21:57.499039Z

Source-reported events for the cited work

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Observation 7358ac7c-b5e0-4b22-af33-cdcbdcf233fa · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Gman: A graph multi-attention network for traffic prediction

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.710167Z

Source-reported events for the cited work

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Observation 02c47803-6d57-4914-81c0-e19a9d4f78b6 · outbound

This paper cites Vector autoregressive models for multivariate time series.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Vector autoregressive models for multivariate time series

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:21:57.700528Z

Source-reported events for the cited work

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Observation b6384a65-858e-4119-8281-53a31f0dc974 · outbound

This paper cites @esa (Ref.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting @esa (Ref

Reference 54

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Source-reported events for the cited work

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Observation 4d074a62-0b6a-4e03-a129-83ea1350a173 · outbound

This paper cites an unresolved cited work.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T16:21:57.510676Z

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Observation d420b346-9ba0-429b-9111-8fd336c7893d · outbound

This paper cites an unresolved cited work.

T-Graphormer: Using Transformers for Spatiotemporal Forecasting Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-10T16:21:57.678335Z

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Pith citing papers

Observation 7da3c912-7bcf-4219-b828-43a9a789c888 · inbound

Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction cites this paper.

Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction T-Graphormer: Using Transformers for Spatiotemporal Forecasting

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T19:08:18.190212Z

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