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Paper Citation Record · LEDGER

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.02525.

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

pith.paper-citation-record.v1
2412.02525 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:24:18.697041Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:37:53.815881Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:37:54.801817Z

Reference resolution

27 of 27 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d5a3363-50fe-4ea6-bb1e-a0e1208ed4b6 · outbound

This paper cites Long short-term memory.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Long short-term memory

Reference 1

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Observation aae823c2-9df1-497e-bf0c-48f18f11f06f · outbound

This paper cites Deep learning with long short-term memory networks for financial market predictions.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Deep learning with long short-term memory networks for financial market predictions

Reference 2

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raw_fallback, observed 2026-08-11T23:24:18.910758Z

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.

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Observation 71a07151-4907-4fad-b17e-e9e3052de754 · outbound

This paper cites Backpropagation applied to handwritten zip code recognition.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Backpropagation applied to handwritten zip code recognition

Reference 3

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Observation 9c2fee53-0f45-4e0f-acb1-ce78d0be8025 · outbound

This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Temporal fusion transformers for interpretable multi-horizon time series forecasting

Reference 4

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Observation 2193fbac-2a83-4954-bbe0-30721e0e6dae · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 5

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Observation 17be3fc1-2ae5-40cf-b4fa-a89ae1d441f3 · outbound

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

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting

Reference 6

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Observation a92f14d9-cbeb-40fd-9dc1-fa40b79def4f · outbound

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

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 7

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Observation 935d9ff0-5ffa-4aa1-877e-0f12bab99bde · outbound

This paper cites A temporal fusion transformer for short-term freeway traffic speed multistep prediction.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data A temporal fusion transformer for short-term freeway traffic speed multistep prediction

Reference 8

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raw_fallback, observed 2026-08-11T23:24:18.880293Z

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.

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Observation f7025825-f4c3-4119-a2d1-19a7dc35f2e0 · outbound

This paper cites Temporal fusion transformers model for traffic flow prediction.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Temporal fusion transformers model for traffic flow prediction

Reference 9

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raw_fallback, observed 2026-08-11T23:24:18.871803Z

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.

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Observation c10ebe44-6366-47c5-8853-244dac7b1f14 · outbound

This paper cites MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention

Reference 10

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Observation 3b9e8a42-cf7b-4647-a55f-367828e2980a · outbound

This paper cites Deepar: Probabilis- tic forecasting with autoregressive recurrent networks.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Deepar: Probabilis- tic forecasting with autoregressive recurrent networks

Reference 11

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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.

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Observation 3682ae13-e97f-4682-a3a4-b0a4c87e97de · outbound

This paper cites Asset bundling for hierarchical forecasting of wind power generation.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Asset bundling for hierarchical forecasting of wind power generation

Reference 12

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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.

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Observation 105b7248-251c-4197-8f0a-b5aa5aa86630 · outbound

This paper cites Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture

Reference 13

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

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Observation 585ced4a-f6ea-4cfd-83ad-98aa7ac6b1c9 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis, 2023.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Timesnet: Temporal 2d-variation modeling for general time series analysis, 2023

Reference 14

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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.

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Observation 16434a6f-7f6c-40fe-ac54-59c54a1d0669 · outbound

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

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Chronos: Learning the Language of Time Series

Reference 15

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Observation 017ef9bf-33d8-4501-b063-773d4c8b49a3 · outbound

This paper cites A Multi-Horizon Quantile Recurrent Forecaster.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data A Multi-Horizon Quantile Recurrent Forecaster

Reference 16

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Observation 2abfdf85-beb5-490d-9afa-9ed1338df350 · outbound

This paper cites Large language models are zero-shot time series forecasters.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Large language models are zero-shot time series forecasters

Reference 17

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

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Observation e2d3866f-205d-4e97-a8b5-e36f2488312e · outbound

This paper cites Empowering Time Series Analysis with Large Language Models: A Survey.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Empowering Time Series Analysis with Large Language Models: A Survey

Reference 18

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Observation a0858949-9909-48c2-af05-ee082b63ee3a · outbound

This paper cites Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 19

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Observation de630364-348d-4f2a-9fb5-d846063919f5 · outbound

This paper cites Where Would I Go Next? Large Language Models as Human Mobility Predictors.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Where Would I Go Next? Large Language Models as Human Mobility Predictors

Reference 20

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Observation 6dda853e-857a-4eff-9526-10e986a490f9 · outbound

This paper cites Large Language Models are Few-Shot Health Learners.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Large Language Models are Few-Shot Health Learners

Reference 21

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Observation 0a71499d-765a-484a-9661-97bb4069d18a · outbound

This paper cites Promptcast: A new prompt-based learning paradigm for time series forecasting.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Promptcast: A new prompt-based learning paradigm for time series forecasting

Reference 22

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

This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

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 cfa60f85-73ba-472a-88e3-b76a5b2da543 · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

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

Reference 24

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Observation f649938c-1c5c-48e3-add8-52646b8fcd2f · outbound

This paper cites an unresolved cited work.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Unresolved cited work

Reference 25

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

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Observation 265fa3a5-7938-478e-a3fe-18dde1e19465 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data LoRA: Low-Rank Adaptation of Large Language Models

Reference 26

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Observation 9dad5ba8-d296-4680-a397-f0f0c9cae38c · outbound

This paper cites Holiday-Encoding Prompt.

LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data Holiday-Encoding Prompt

Reference 27

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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.

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Pith citing papers

Observation d2d13c89-cffc-4d38-84ff-1b3208100738 · inbound

TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting cites this paper.

TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data

Reference 40

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local_arxiv, observed 2026-08-06T17:37:54.873654Z

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.

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