Paper Citation Record · LEDGER

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2308.08469.

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

pith.paper-citation-record.v1
2308.08469 v6

Coverage vector

measured 0 of 0 reference resolution

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measured 32 of 32 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:15:53.261404Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:29:56.378068Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation 08d5ba5b-4ff1-4031-b237-39505effb8db · inbound

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models cites this paper.

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 76

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Observation d4e31280-9721-4896-b3b3-96f0f7b01893 · inbound

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

A decoder-only foundation model for time-series forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:07:21.293631Z

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Observation fba9aa84-3fa3-4139-a2f0-43493bea4703 · inbound

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

Deep Time Series Models: A Comprehensive Survey and Benchmark LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 204

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Observation 48b55c78-aae2-4dd4-9d71-3594c086cb0c · inbound

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting cites this paper.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2024

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Observation bafed35f-5b54-4642-8da9-ab81abfa86b7 · inbound

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

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 23

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Observation d5daccd3-66cc-4e11-b805-fde7a5f2f607 · inbound

Federated Foundation Models on Heterogeneous Time Series cites this paper.

Federated Foundation Models on Heterogeneous Time Series LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 3

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Observation 80d25713-9c4e-4104-bab1-3bdb3440319b · inbound

A Survey on Time-Series Distance Measures cites this paper.

A Survey on Time-Series Distance Measures LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 42

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Observation a5a09acb-d279-489e-bef6-31c9dd1e5b44 · inbound

Time Series Language Model for Descriptive Caption Generation cites this paper.

Time Series Language Model for Descriptive Caption Generation LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 4

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MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models cites this paper.

MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 76

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Observation 3543de33-41a1-4dac-9c73-338bbaed44f8 · inbound

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics cites this paper.

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 10

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Observation 237d6e51-1397-4689-8de1-4b867bbfa6b9 · inbound

LAST SToP For Modeling Asynchronous Time Series cites this paper.

LAST SToP For Modeling Asynchronous Time Series LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 9

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Observation 9450f5bb-598e-4035-9149-d569fdde36fb · inbound

A Multi-Task Learning Approach to Linear Multivariate Forecasting cites this paper.

A Multi-Task Learning Approach to Linear Multivariate Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2

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Observation 51f9a2dd-98f9-406f-92f7-b21d24da0a36 · inbound

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting cites this paper.

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2

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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 LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 8

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Observation cc6d596e-4dbd-4685-a75e-fc7ab8783f7a · inbound

MoTime: A Dataset Suite for Multimodal Time Series Forecasting cites this paper.

MoTime: A Dataset Suite for Multimodal Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 7

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Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting cites this paper.

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 6

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Observation 194439d7-a13d-49ff-a37d-c7d662b9dadf · inbound

Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting cites this paper.

Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 5

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Observation bf959378-08dd-4002-a059-862c89b7d812 · inbound

DELPHYNE: A Pre-Trained Model for General and Financial Time Series cites this paper.

DELPHYNE: A Pre-Trained Model for General and Financial Time Series LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 7

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Observation b90301bb-ad07-4131-ac9a-1cd7d2cf88fe · 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 LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 44

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From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 12

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Observation aca4bdee-92f2-4b99-a042-a9ed9759b25f · 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 LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 28

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Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services cites this paper.

Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 56

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Observation a915987f-31ea-4131-aa1e-cb411ed9b46a · inbound

Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives? cites this paper.

Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives? LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 6

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A Survey of AIOps in the Era of Large Language Models cites this paper.

A Survey of AIOps in the Era of Large Language Models LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 11

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

Discrete Prototypical Memories for Federated Time Series Foundation Models LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 5

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7999305c-3c86-4eb0-a13d-2362115d2e67 · inbound

ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification cites this paper.

ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:26:00.013426Z

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Observation 5bc36576-23d4-49cb-918a-35b10cddb82a · inbound

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting cites this paper.

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:00:22.427782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1fe0a5ac-c53f-4cf3-b99e-210555e741d0 · inbound

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy cites this paper.

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 1

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c4e7b7bf-2562-47bd-8f89-98e1fb46c753 · inbound

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model? cites this paper.

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model? LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2

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Source-reported events for the cited work

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Observation 36560881-e89f-46ad-a9df-2b71c7a56312 · inbound

Pretrained Time-Series Foundation Models for Financial Return Forecasting cites this paper.

Pretrained Time-Series Foundation Models for Financial Return Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:29:56.379690Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d82819b7-7348-44b2-9904-0c3abfaab15a · inbound

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

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 12

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 887289b8-b4b7-4b54-b827-187644554e8e · inbound

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

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 95

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