Paper Citation Record · LEDGER

Transformers and Their Roles as Time Series Foundation Models

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2502.03383.

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

pith.paper-citation-record.v1
2502.03383 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:01:32.397410Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:40:58.659411Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:40:59.236173Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1eef3191-2bf8-4744-9622-0111f367d607 · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

Transformers and Their Roles as Time Series Foundation Models Unified Training of Universal Time Series Forecasting Transformers

Reference 1

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Observation 7a8d239e-118b-4c72-9bd5-3010306a5e27 · outbound

This paper cites Chronos: Learning the Language of Time Series.

Transformers and Their Roles as Time Series Foundation Models Chronos: Learning the Language of Time Series

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7a835d-2efa-4a34-99ee-0267e0c79525 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

Transformers and Their Roles as Time Series Foundation Models Foundation models for time series analysis: A tutorial and survey

Reference 3

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Observation 7b85127f-55fd-4b81-ac26-7167ae792cfb · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Transformers and Their Roles as Time Series Foundation Models A decoder-only foundation model for time-series forecasting

Reference 4

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Observation d8291d35-c29f-41f5-aa6f-e9cdde5d763f · outbound

This paper cites Lag-llama: Towards foundation models for time series forecasting.

Transformers and Their Roles as Time Series Foundation Models Lag-llama: Towards foundation models for time series forecasting

Reference 5

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Observation fb71b063-c214-4cba-be9d-987b5a63932e · outbound

This paper cites Time series analysis.

Transformers and Their Roles as Time Series Foundation Models Time series analysis

Reference 6

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Observation e9319865-8c1e-4d17-8b7c-3596a1ac1380 · outbound

This paper cites Time series techniques for economists.

Transformers and Their Roles as Time Series Foundation Models Time series techniques for economists

Reference 7

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Observation 198ca922-16e5-4ea6-b777-fbadaef05ebd · outbound

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Transformers and Their Roles as Time Series Foundation Models Unresolved cited work

Reference 8

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Observation 8821df1b-0f04-453f-a1de-609b3bb33c9c · outbound

This paper cites Completely analytical interactions: constructive descrip- tion.

Transformers and Their Roles as Time Series Foundation Models Completely analytical interactions: constructive descrip- tion

Reference 9

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Observation 249d4ec4-419f-4b8b-8785-c4e7d5463206 · outbound

This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Transformers and Their Roles as Time Series Foundation Models Transformers as statisticians: Provable in-context learning with in-context algorithm selection

Reference 10

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Observation 362a0769-d062-41eb-8dae-5885b0b33780 · outbound

This paper cites Transformers learn in-context by gradient descent.

Transformers and Their Roles as Time Series Foundation Models Transformers learn in-context by gradient descent

Reference 11

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Observation 252e1efa-eeb4-479d-b527-3e9d13dd7958 · outbound

This paper cites Transformers as algorithms: Generalization and stability in in-context learning.

Transformers and Their Roles as Time Series Foundation Models Transformers as algorithms: Generalization and stability in in-context learning

Reference 12

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Observation 5eb28838-b28f-4039-b13a-9525e9d81771 · outbound

This paper cites One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention.

Transformers and Their Roles as Time Series Foundation Models One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention

Reference 13

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Observation a368d547-930f-451c-9562-6e96e7e90cc7 · outbound

This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

Transformers and Their Roles as Time Series Foundation Models Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 14

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Source-reported events for the cited work

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Observation 37305333-afcf-4e39-adc0-5f1c0f6c460e · outbound

This paper cites Trained transformers learn linear models in-context.

Transformers and Their Roles as Time Series Foundation Models Trained transformers learn linear models in-context

Reference 15

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Observation 3a60c466-fb29-4fdb-a62e-e077ef317a1e · outbound

This paper cites Language models are few-shot learn- ers.

Transformers and Their Roles as Time Series Foundation Models Language models are few-shot learn- ers

Reference 16

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Observation ef7bc9fb-ac22-4961-8d05-fbe651c1842a · outbound

This paper cites What can transformers learn in- context? a case study of simple function classes.

Transformers and Their Roles as Time Series Foundation Models What can transformers learn in- context? a case study of simple function classes

Reference 17

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Observation c58bf8a3-d78c-41d8-88ff-4cac77adadeb · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Transformers and Their Roles as Time Series Foundation Models Roformer: Enhanced transformer with rotary position embedding

Reference 18

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Observation 95b2c350-a328-46eb-8036-9eef6af7b4f5 · outbound

This paper cites What learning algo- rithm is in-context learning? investigations with linear models.

Transformers and Their Roles as Time Series Foundation Models What learning algo- rithm is in-context learning? investigations with linear models

Reference 19

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Source-reported events for the cited work

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Observation 253d1545-1299-4b93-9269-aff5e5d781cb · outbound

This paper cites Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining.

Transformers and Their Roles as Time Series Foundation Models Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 20

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Observation 1f2c86cb-6f51-4f1a-9d1e-2723d05b16e4 · outbound

This paper cites Learning Spectral Methods by Transformers.

Transformers and Their Roles as Time Series Foundation Models Learning Spectral Methods by Transformers

Reference 21

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Source-reported events for the cited work

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Observation 72f0c262-72e3-4bd2-b2c0-5fdc5be8147c · outbound

This paper cites Replacing softmax with ReLU in Vision Transformers.

Transformers and Their Roles as Time Series Foundation Models Replacing softmax with ReLU in Vision Transformers

Reference 22

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Observation 05e285ee-c4c2-4156-9d1b-733867176421 · outbound

This paper cites Sparse Attention with Linear Units.

Transformers and Their Roles as Time Series Foundation Models Sparse Attention with Linear Units

Reference 23

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Observation d5e5a6e9-e6a1-4281-bb3e-37ba8a4b3f99 · outbound

This paper cites A Study on ReLU and Softmax in Transformer.

Transformers and Their Roles as Time Series Foundation Models A Study on ReLU and Softmax in Transformer

Reference 24

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Observation 1d2e0051-9687-47d8-91f5-b7873abd8aa9 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Transformers and Their Roles as Time Series Foundation Models LLaMA: Open and Efficient Foundation Language Models

Reference 25

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Observation 6504a57d-5b23-40cc-b45d-3aa0ab865f89 · outbound

This paper cites Strategies to leverage foundational model knowledge in object affordance grounding.

Transformers and Their Roles as Time Series Foundation Models Strategies to leverage foundational model knowledge in object affordance grounding

Reference 26

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Observation 5096176a-01ba-48ae-87f2-bf46c5710ed7 · outbound

This paper cites Monash Time Series Forecasting Archive.

Transformers and Their Roles as Time Series Foundation Models Monash Time Series Forecasting Archive

Reference 27

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Observation dbb14712-23e3-4a97-b694-2091d6378844 · outbound

This paper cites Gluonts: Probabilistic and neural time series modeling in python.

Transformers and Their Roles as Time Series Foundation Models Gluonts: Probabilistic and neural time series modeling in python

Reference 28

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Observation 459c0ff3-9305-49d0-96d5-28c998086949 · outbound

This paper cites Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecasting.

Transformers and Their Roles as Time Series Foundation Models Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecasting

Reference 29

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Observation da57364b-92c7-47bc-9797-ca4da4a845fc · outbound

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

Transformers and Their Roles as Time Series Foundation Models Modeling long-and short-term temporal patterns with deep neural networks

Reference 30

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Observation 284c798f-2510-4e5c-b6fe-157fd5eb67aa · outbound

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

Transformers and Their Roles as Time Series Foundation Models iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 31

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Observation 8039a3c6-493e-4238-bcb1-ddb8df34af41 · outbound

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

Transformers and Their Roles as Time Series Foundation Models A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 32

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Observation f1ab3aa6-3d67-469d-8989-2b6c8ce5647a · outbound

This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting.

Transformers and Their Roles as Time Series Foundation Models Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:01:32.592268Z

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Observation 64b2ac71-9cd4-42de-b4fe-26e85ddf615e · outbound

This paper cites How Transformers Learn Causal Structure with Gradient Descent.

Transformers and Their Roles as Time Series Foundation Models How Transformers Learn Causal Structure with Gradient Descent

Reference 34

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Observation 8dc65c7c-f971-45e3-83fb-b26e0d786c69 · outbound

This paper cites How do Transformers perform In-Context Autoregressive Learning?.

Transformers and Their Roles as Time Series Foundation Models How do Transformers perform In-Context Autoregressive Learning?

Reference 35

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Observation 65f8d99b-db7a-4532-b337-450e8a088cf2 · outbound

This paper cites High-dimensional statistics: A non-asymptotic viewpoint, volume 48.

Transformers and Their Roles as Time Series Foundation Models High-dimensional statistics: A non-asymptotic viewpoint, volume 48

Reference 36

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Source-reported events for the cited work

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Observation 320e9eca-ec15-49d9-94f9-0703e9e29b05 · outbound

This paper cites Learning from weakly dependent data under dobrushin’s condition.

Transformers and Their Roles as Time Series Foundation Models Learning from weakly dependent data under dobrushin’s condition

Reference 37

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Observation 64dbc17b-1b68-4c49-a9af-544a45152d00 · outbound

This paper cites Concentration inequalities for functions of gibbs fields with application to diffraction and random gibbs measures.

Transformers and Their Roles as Time Series Foundation Models Concentration inequalities for functions of gibbs fields with application to diffraction and random gibbs measures

Reference 38

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Observation a92c446d-cd8e-4837-b1dc-d786e1fe5501 · outbound

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Transformers and Their Roles as Time Series Foundation Models Unresolved cited work

Reference 39

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Observation 31ff3abe-d1b0-468b-ae5e-b63dbfe2357d · outbound

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Transformers and Their Roles as Time Series Foundation Models Unresolved cited work

Reference 40

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Observation b94cdecb-c20b-422d-b6a8-797be361657c · outbound

This paper cites to get P(T ) j , for j = 1, · · ·, n.

Transformers and Their Roles as Time Series Foundation Models to get P(T ) j , for j = 1, · · ·, n

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:01:32.543540Z

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Observation 386fe85a-090e-49f9-87fd-4acc5f13bce1 · outbound

This paper cites We assume that for each j ∈ [n], (zj,t) has marginals equal to some distribution D for t = 1, · · ·, T.

Transformers and Their Roles as Time Series Foundation Models We assume that for each j ∈ [n], (zj,t) has marginals equal to some distribution D for t = 1, · · ·, T

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:01:32.534689Z

Source-reported events for the cited work

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Pith citing papers

Observation 731d3c29-9af1-406b-9439-20e3501f0d56 · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Transformers and Their Roles as Time Series Foundation Models

Reference 44

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Observation 3bbd881e-cfe7-41b4-8457-cae039aa925c · inbound

Large Causal Models for Temporal Causal Discovery cites this paper.

Large Causal Models for Temporal Causal Discovery Transformers and Their Roles as Time Series Foundation Models

Reference 52

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