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Paper Citation Record · LEDGER

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2310.04948.

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

pith.paper-citation-record.v1
2310.04948 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:37:00.793721Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.231037Z

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Outbound references

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Pith citing papers

Observation f37eb6df-cda2-4e00-911f-bceea0b3811c · inbound

Deep Time Series Models: A Comprehensive Survey and Benchmark cites this paper.

Deep Time Series Models: A Comprehensive Survey and Benchmark TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 55

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arxiv_id, observed 2026-05-23T23:05:51.467777Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T23:03:45.096751Z digest=sha256:67deb8e089b07b3acb67e1a4733c17cabd472d732dc260e28a88cfb5e359c52c

Observation fb4a7b4a-08ae-4b91-92d2-b5abd470b8f2 · inbound

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis cites this paper.

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 35

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arxiv_id, observed 2026-05-23T19:45:47.124040Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T19:45:39.130509Z digest=sha256:572e6b0ef7945d6a5d15c1110a35c713709a3ccc6fd146233849a63f5700d79a

Observation eb54d148-09b5-4a63-951d-64f3fc3b8cd0 · inbound

Large Language Models are Few-shot Multivariate Time Series Classifiers cites this paper.

Large Language Models are Few-shot Multivariate Time Series Classifiers TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 24

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source=pdf_text observed=2026-08-10T00:37:00.793721Z digest=sha256:4c395ba80dd41892bdb02591f2ceb4a91ac4e0b8e455d0316772353a33e66684

Observation 71149463-66eb-4dbc-ba12-759e4e49ff40 · inbound

LAST SToP For Modeling Asynchronous Time Series cites this paper.

LAST SToP For Modeling Asynchronous Time Series TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 8

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source=arxiv_source observed=2026-08-09T14:02:48.342995Z digest=sha256:83ecbae92924c2053957b770237727dac75d9fdf273845daf11c8b68d4b1f716

Observation bd92e843-f503-420c-9272-e100657fb887 · inbound

Context information can be more important than reasoning for time series forecasting with a large language model cites this paper.

Context information can be more important than reasoning for time series forecasting with a large language model TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 15

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source=pdf_text observed=2026-08-08T18:22:22.885319Z digest=sha256:6d9afc78031d1c63c44e14c4d7c5cfb585d32bbfb1f04a8ca09b4cb7bd79016b

Observation 8fc3e7cf-fdaf-48cb-8bdc-2362a821a5d3 · inbound

TOKON: TOKenization-Optimized Normalization for time series analysis with a large language model cites this paper.

TOKON: TOKenization-Optimized Normalization for time series analysis with a large language model TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 9

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source=pdf_text observed=2026-08-08T18:22:10.277176Z digest=sha256:8554fc0dd32a5b9316c634ff0638a719f3b600fcc2e83b76e23f4a9bb3fb53d4

Observation 7a02a285-51cc-4b15-a390-cf430340fcc4 · inbound

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges cites this paper.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 49

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source=pdf_text observed=2026-08-07T15:26:54.471141Z digest=sha256:f90afbd681543e1c841858fc4b26354b9a56b380c340a40fe1e363f62c9754fc

Observation dca585c9-0c93-45da-89b6-ae6114c79f4a · inbound

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection cites this paper.

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 27

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Observation 6a9f81e4-710a-4225-88e1-6fb4db85126c · inbound

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting cites this paper.

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 33

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source=pdf_text observed=2026-08-06T18:01:26.539715Z digest=sha256:d9c237293159f642be149cfbe93c1a3f3d56f30cb92635ef1bbdce4ef24590cd

Observation 5c2df10b-0997-45c6-a123-fb8e6477820e · inbound

Fusing Large Language Models with Temporal Transformers for Time Series Forecasting cites this paper.

Fusing Large Language Models with Temporal Transformers for Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 2023

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source=pdf_text observed=2026-08-06T17:43:36.297956Z digest=sha256:c01653c618dfdf606061f124dc8380f56b8cca2c902bbda248cdce50e2365461

Observation c88630ce-e66e-4718-be14-4e2ecd7e5649 · inbound

Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting cites this paper.

Reprogramming Vision Foundation Models for Spatio-Temporal Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 5

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source=arxiv_source observed=2026-08-06T17:46:42.029777Z digest=sha256:6233fc02c1d45c93adaff67645690f948b3c0a3414ce978711d5371211635c78

Observation 1c1804c7-6a27-4281-b914-3a2ca9111ab9 · inbound

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting cites this paper.

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 8

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Observation 0a1e5efe-ee16-46f1-b5b1-ae1ef6b27901 · inbound

LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction cites this paper.

LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 62

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Observation 1ea95ebc-01cc-42aa-8e9d-c9c5f53f4c41 · inbound

Foundation Models for Demand Forecasting via Dual-Strategy Ensembling cites this paper.

Foundation Models for Demand Forecasting via Dual-Strategy Ensembling TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 3

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source=pdf_text observed=2026-08-06T12:09:39.411328Z digest=sha256:f8468353a148ec355472e2803552cbcb6ef38122d677127ad674c7423bc4a618

Observation 94b5f497-58bb-4c6d-9d0d-ed106294dbb6 · inbound

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating cites this paper.

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 12

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Observation 1645107e-eabe-4401-b9f8-e7fe40ce0e16 · inbound

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting cites this paper.

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 4

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source=pdf_text observed=2026-08-05T13:29:00.791648Z digest=sha256:26361b130d9c261da656ba8eb1dd45fc9433a68bf2a1e9958e49857d77642da3

Observation a4d736a0-0452-486d-a5aa-85c5f31d376f · inbound

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting cites this paper.

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 6

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arxiv_id, observed 2026-05-25T08:25:34.155340Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 11f089b3-86bc-4e12-8093-03ad70f608d8 · inbound

Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction cites this paper.

Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 2024

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Observation 7cb01dd6-19f5-4597-9867-9583270727ba · inbound

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models cites this paper.

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 2

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arxiv_id, observed 2026-05-22T12:36:32.511386Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4b2a3109-09d0-4f7a-af06-a22400cdc804 · inbound

AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting cites this paper.

AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 4

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arxiv_id, observed 2026-05-17T23:10:26.178196Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 16a61787-7cee-4c6c-8b88-df90f1660120 · inbound

Discrete Prototypical Memories for Federated Time Series Foundation Models cites this paper.

Discrete Prototypical Memories for Federated Time Series Foundation Models TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 4

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arxiv_id, observed 2026-05-10T23:35:52.028972Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c6f35693-62b5-468c-9965-13efab01ad94 · inbound

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale cites this paper.

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 8

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arxiv_id, observed 2026-05-11T10:36:04.345350Z

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Observation f5906bd9-77a5-4dfb-81a5-6de4cc45eed0 · inbound

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning cites this paper.

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 3

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arxiv_id, observed 2026-05-11T20:36:09.707554Z

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Observation ba5c7cc4-386d-4f40-be46-a72ebd7dab36 · inbound

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling cites this paper.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 32

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Observation 3c785689-a48d-46af-bd12-85eeca7e563d · inbound

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models cites this paper.

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 4

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arxiv_id, observed 2026-07-02T12:46:57.002897Z

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Observation 234a9197-0779-4544-b390-d3e072665460 · inbound

$\text{DT}^2$: Decision-Targeted Digital Twins cites this paper.

$\text{DT}^2$: Decision-Targeted Digital Twins TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 28

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Observation 2abf6c67-add8-4ce4-986b-aa6e9ab09d16 · inbound

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 28

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arxiv_id, observed 2026-07-03T17:38:43.411552Z

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Observation 678a0003-5bf1-4545-b755-ff2a71d59e80 · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 5

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Observation bf7f08f0-f680-4a02-b9ac-cf8563d664c9 · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 5

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Observation 59b67d14-94d3-4e78-9a4e-8cb9d0a79f3a · inbound

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting cites this paper.

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 3

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Observation 08c1f456-385a-4784-bc24-f87f0f068ec8 · inbound

A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series cites this paper.

A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 1

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