Paper Citation Record · LEDGER

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data

As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2411.18731.

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

pith.paper-citation-record.v1
2411.18731 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:59:32.358563Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy10
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 90179753-338e-4f21-bc6a-9d64d633b3f3 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Constitutional AI: Harmlessness from AI Feedback

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.245185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a813f47-cd57-4db6-9353-75578c0bdd41 · outbound

This paper cites an unresolved cited work.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:59:32.949769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04a697e2-d5fc-4985-9cc4-9390adc5aed6 · outbound

This paper cites Languagemodelsarefew-shotlearners.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Languagemodelsarefew-shotlearners

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.937798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ab7e4b5-72d0-4810-8c57-294ad77de5c9 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data PaLM: Scaling Language Modeling with Pathways

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.260260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb0cbcc-2c16-45c1-a414-bd2fc93b3458 · outbound

This paper cites The economic potential of generative ai: The next productivity frontier.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data The economic potential of generative ai: The next productivity frontier

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.926261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 040b979f-08d2-4344-a078-d8b450c7ecca · outbound

This paper cites Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.269622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37f74464-08ba-4b13-b535-2e1c7ae470ac · outbound

This paper cites A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:59:32.766154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef77a28c-102a-4f87-904d-4f4326466498 · outbound

This paper cites A comparison of tcn and lstm models in detecting anomalies in time seriesdata,in:2021IEEEInternationalConferenceonBigData(Big Data), pp.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data A comparison of tcn and lstm models in detecting anomalies in time seriesdata,in:2021IEEEInternationalConferenceonBigData(Big Data), pp

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.279734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77c2daa2-e982-4c86-8927-43be6f423e36 · outbound

This paper cites Vulnerability detection in smart contracts using deep learning, in: 2022 IEEE 46th Annual Computers, Software, and Applications Conference(COMPSAC),pp.1249–1255.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Vulnerability detection in smart contracts using deep learning, in: 2022 IEEE 46th Annual Computers, Software, and Applications Conference(COMPSAC),pp.1249–1255

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-12T10:59:32.681525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ed3a4b6-5f6a-4d30-9ada-7dd38fa96897 · outbound

This paper cites an unresolved cited work.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Unresolved cited work

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T10:59:32.596195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9de9cf9a-2a79-4c1b-8518-bb132be786bc · outbound

This paper cites Deep learning-based time-series analysis for detecting anomalies in internet of things.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Deep learning-based time-series analysis for detecting anomalies in internet of things

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.915677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e365eb3-0c2b-4248-a807-5f13a5b51245 · outbound

This paper cites an unresolved cited work.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Unresolved cited work

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T10:59:32.904341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2f2718c9-2249-4d5a-9e4b-11728ad13b08 · outbound

This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.298932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6d7cdd-3daf-4e98-ae3e-7072b529e100 · outbound

This paper cites an unresolved cited work.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:59:32.893573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e88a019-a13f-4c8e-9b44-4b9796cc0035 · outbound

This paper cites Lever: Learning to verify language-to-code generation with execution, in: International Conference on Machine Learning, PMLR.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Lever: Learning to verify language-to-code generation with execution, in: International Conference on Machine Learning, PMLR

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.882085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e794723f-fb67-4c54-ae75-769da1efb59b · outbound

This paper cites Training language models to follow instructions with human feedback.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Training language models to follow instructions with human feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.312094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f100966a-120c-41fe-9aad-e2995ec58152 · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.316278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 941da4de-23c7-464d-b91a-e9abc7e375ac · outbound

This paper cites True few-shot learning with languagemodels.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data True few-shot learning with languagemodels

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.863970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 766cb6e0-a9a2-4455-a6d3-9305ac15d9c9 · outbound

This paper cites Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation, in: Proceedings of the 31st ACM International Conference on Multime- dia, pp.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation, in: Proceedings of the 31st ACM International Conference on Multime- dia, pp

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.851846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8ae8e0e-0840-4a5d-a6fa-62ba4696bb58 · outbound

This paper cites Prompt programming for large language models: Beyond the few-shot paradigm, in: Extended Ab- stracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Prompt programming for large language models: Beyond the few-shot paradigm, in: Extended Ab- stracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.841063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 033e350d-ffe4-4f13-81ab-3b1310a031c9 · outbound

This paper cites Top generative ai statistics for 2023.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Top generative ai statistics for 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.829870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f577d46-986c-409f-9a9f-0b6f7c6b8edd · outbound

This paper cites Global sensitivity analysis: the primer.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Global sensitivity analysis: the primer

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.818668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b1ed1e4-f40b-418a-9768-b1ac3b329181 · outbound

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

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data LLaMA: Open and Efficient Foundation Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.338254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bcc5b5e-821d-47bb-b5aa-041b8716278b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.343509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a758480-bd44-42e6-b7e6-29976fe51766 · outbound

This paper cites Expectation vs.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Expectation vs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:59:32.806780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bbf426cf-9d02-4344-b980-43b4eedce01e · outbound

This paper cites Is ChatGPT a Good NLG Evaluator? A Preliminary Study.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Is ChatGPT a Good NLG Evaluator? A Preliminary Study

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.351325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3ffa158-a042-4cef-bfff-2e82c6dbc1bf · outbound

This paper cites an unresolved cited work.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.354920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c3f439d-aab8-4948-8ac6-7ec744326487 · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data Large Language Models Are Human-Level Prompt Engineers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T10:59:32.358563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.