用扩散模型生成金融图表，预测股价走势。
Exploring Diffusion Models for Generative Forecasting of Financial Charts
- 将时间序列转为图像，用扩散模型生成下一帧图表。
- 通过对比生成图与真实图评估预测效果。
- 为金融预测提供新思路，适合对生成模型感兴趣者。
生成模型的最新进展在图像、视频生成等任务中取得显著成果，但金融领域仍以时间序列分析和变换器模型为主，较少应用生成模型。本文提出一种新方法：将时间序列数据视为单一图像模式，借助文本到图像模型预测股票价格趋势。不同于以往使用ResNet或ViT学习和分类图表模式的方法，我们尝试从当前图表图像和指令提示生成下一阶段的图表图像。此外，提出一种简单方法评估生成图像与真实图像的差异。实验表明，该方法展现了生成模型在金融领域的潜力，激励未来研究解决现有局限并拓展其应用范围。
原文摘要 · Abstract (English)
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.
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