# Exploring Diffusion Models for Generative Forecasting of Financial Charts

arXiv: https://arxiv.org/abs/2509.02308 · HTML: https://mlsanity.com/paper/2509.02308/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Generating Realistic Images from In-the-wild Sounds](https://mlsanity.com/paper/2309.02405/) (2023)
- [Stock Chart Pattern recognition with Deep Learning](https://mlsanity.com/paper/1808.00418/) (2018)
- [Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction](https://mlsanity.com/paper/2411.05790/) (2024)

## Abstract (arXiv)

Recent advances in generative models have enabled significant progress in tasks such as generating and editing images from text, as well as creating videos from text prompts, and these methods are being applied across various fields. However, in the financial domain, there may still be a reliance on time-series data and a continued focus on transformer models, rather than on diverse applications of generative models. In this paper, we propose a novel approach that leverages text-to-image model by treating time-series data as a single image pattern, thereby enabling the prediction of stock price trends. Unlike prior methods that focus on learning and classifying chart patterns using architectures such as ResNet or ViT, we experiment with generating the next chart image from the current chart image and an instruction prompt using diffusion models. Furthermore, we introduce a simple method for evaluating the generated chart image against ground truth image. We highlight the potential of leveraging text-to-image generative models in the financial domain, and our findings motivate further research to address the current limitations and expand their applicability.