Paper Citation Record · LEDGER

Exploring Diffusion Models for Generative Forecasting of Financial Charts

As of 21 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2509.02308.

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

pith.paper-citation-record.v1
2509.02308 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:42:42.572100Z

measured 23 of 23 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 2026-08-16T00:07:52.958984Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T00:07:53.897609Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy10
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 894c23a8-e890-454e-b382-185339387428 · outbound

This paper cites GPT-4 Technical Report.

Exploring Diffusion Models for Generative Forecasting of Financial Charts GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.460677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f72fe0cd-4553-4b1c-a82c-cf796a4fb12a · outbound

This paper cites Cnn-based stock price forecasting by stock chart images.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Cnn-based stock price forecasting by stock chart images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.922544Z

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 8cb9e917-1ede-49b4-9232-e5366f38c831 · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

Exploring Diffusion Models for Generative Forecasting of Financial Charts InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.470069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1952e219-9cac-4e1d-8c76-96900fcf425e · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.475136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06617249-be4a-4891-8e2f-d9d83c613c09 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Exploring Diffusion Models for Generative Forecasting of Financial Charts An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.480388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e0e0e16-a0e7-42dd-b092-7337a9787f27 · outbound

This paper cites Long short-term memory.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Long short-term memory

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.486632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cefc8807-f63d-4eea-a95b-c9414820fde7 · outbound

This paper cites Deep residual learning for image recognition.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Deep residual learning for image recognition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.491579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 266c79b5-587a-4f10-a427-ece23bac924f · outbound

This paper cites Imagic: Text-Based Real Image Editing with Diffusion Models.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Imagic: Text-Based Real Image Editing with Diffusion Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.496222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36653987-1081-42a1-897b-8d34e6cb6159 · outbound

This paper cites Generating realistic images from in-the-wild sounds.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Generating realistic images from in-the-wild sounds

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.880516Z

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 9fa0dc72-2c5b-4f92-8c38-6280a3e9c014 · outbound

This paper cites A stock time series forecasting approach incorporating candlestick patterns and sequence similarity.

Exploring Diffusion Models for Generative Forecasting of Financial Charts A stock time series forecasting approach incorporating candlestick patterns and sequence similarity

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.868064Z

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 4e607c26-a690-4c6c-bbdc-c4e34f4b2085 · outbound

This paper cites Improving stock trading decisions based on pattern recognition using machine learning technology.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Improving stock trading decisions based on pattern recognition using machine learning technology

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.852551Z

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 9fa7bd53-83e8-47a2-a043-1ec0390761fa · outbound

This paper cites Enhancing multi-factor stock selection with transformer networks: A comparative analysis against traditional machine learning models.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Enhancing multi-factor stock selection with transformer networks: A comparative analysis against traditional machine learning models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.828997Z

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 09f33108-809a-48ec-be4a-c386e9d0c508 · outbound

This paper cites Short-term stock market prediction based on candlestick pattern analysis, 2017.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Short-term stock market prediction based on candlestick pattern analysis, 2017

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.813389Z

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 ee74b5f4-ea7d-41f9-9c96-55a0c776f4a9 · outbound

This paper cites Enhancing market trend prediction using convolutional neural networks on japanese candlestick patterns.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Enhancing market trend prediction using convolutional neural networks on japanese candlestick patterns

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.799072Z

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 f3bb493f-4a47-4d13-a5c9-e10949cb16e9 · outbound

This paper cites Image-based time series trend classification using deep learning: A candlestick chart approach.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Image-based time series trend classification using deep learning: A candlestick chart approach

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.786435Z

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 517a4833-7879-4345-90f1-08a616783a1c · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Exploring Diffusion Models for Generative Forecasting of Financial Charts SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.542330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 070f7384-a087-47ed-b417-a23fb77a3b58 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Exploring Diffusion Models for Generative Forecasting of Financial Charts High-resolution image synthesis with latent diffusion models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.548494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6661490d-05c7-4322-8078-315e56ab266c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Gemini: A Family of Highly Capable Multimodal Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.552509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fecefed1-8f60-4ba6-9530-27d333652c07 · outbound

This paper cites Attention is all you need.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Attention is all you need

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.556976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c3f4d1-f544-49d9-8bf9-d19a5b3f3102 · outbound

This paper cites Stock Chart Pattern recognition with Deep Learning.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Stock Chart Pattern recognition with Deep Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T16:42:42.562291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d385c3-0275-42cb-ab2f-d340ddf51c43 · outbound

This paper cites Comparative analysis of lstm, gru, and transformer models for stock price prediction.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Comparative analysis of lstm, gru, and transformer models for stock price prediction

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.749416Z

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 0de887e0-d9b9-4707-af23-fd2011800598 · outbound

This paper cites Pmanet: a time series forecasting model for chinese stock price prediction.

Exploring Diffusion Models for Generative Forecasting of Financial Charts Pmanet: a time series forecasting model for chinese stock price prediction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:42:42.732860Z

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.

Pith citing papers

Observation 515f5ad6-ac65-4da2-afce-37c909e3a146 · inbound

Diffusion Models in Finance: A Survey cites this paper.

Diffusion Models in Finance: A Survey Exploring Diffusion Models for Generative Forecasting of Financial Charts

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:07:53.901229Z

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.