Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 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 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 39 of 39 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:07:03.716929Z

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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External citation measurements

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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

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T23:05:51.467777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T19:45:47.124040Z

Source-reported events for the cited work

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Observation fad16c7d-2a7b-4839-9ac8-18841e024d9c · inbound

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

UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 37

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Observation 33cbab2e-b52f-4a38-b970-2a4b6a3be2d6 · inbound

A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series cites this paper.

A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 4

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Observation cfa60f85-73ba-472a-88e3-b76a5b2da543 · inbound

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data cites this paper.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T23:24:18.688066Z

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Observation cda8cc5b-01cf-45d0-a16f-02b42999ab8a · inbound

Federated Foundation Models on Heterogeneous Time Series cites this paper.

Federated Foundation Models on Heterogeneous Time Series TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 1

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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

Resolution: unresolved; no resolver link, observed 2026-08-10T00:37:00.793721Z

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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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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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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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Observation c75bd127-a595-4aa8-a5ca-d5e479912f38 · inbound

PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting cites this paper.

PV-VLM: A Multimodal Vision-Language Approach Incorporating Sky Images for Intra-Hour Photovoltaic Power Forecasting TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 16

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Observation faf6ea62-6f2b-47df-81a9-b3c5211d5a33 · inbound

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts cites this paper.

Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 24

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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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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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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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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

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:42.029777Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:11.137189Z

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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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Observation b2e35f84-33ab-428a-98b0-b6e63996f39a · inbound

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks cites this paper.

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:14.751830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:28.304185Z

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Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-05T13:29:00.791648Z

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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

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:25:34.155340Z

Source-reported events for the cited work

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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

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:36:32.511386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:10:26.178196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:52.028972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:36:04.345350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:36:09.707554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:57.002897Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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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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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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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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Observation 5d605d38-4835-4c41-8e00-e41656af8b5b · inbound

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers cites this paper.

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 100

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