Summary of A Financial Time Series Denoiser Based on Diffusion Model, by Zhuohan Wang et al.
A Financial Time Series Denoiser Based on Diffusion Model
by Zhuohan Wang, Carmine Ventre
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP); Trading and Market Microstructure (q-fin.TR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to improve data predictability and trading performance in financial time series by utilizing a diffusion model as a denoiser. The conditional diffusion model is used to add and remove noise progressively, reconstructing original data from noisy inputs. Experiments demonstrate that the denoised time series significantly enhance future return classification task performance, yielding more profitable trades with fewer transactions. Classifiers trained on denoised time series can also recognize market noising states and obtain excess returns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Financial time series often have low signal-to-noise ratios, making it hard to predict and make decisions. A new way is proposed to improve this using a diffusion model as a denoiser. This model helps remove noise from data by adding and removing it in steps. The result is better predictions and trading performance. Experiments show that the new approach works well for predicting future returns, leading to more profitable trades with fewer transactions. |
Keywords
» Artificial intelligence » Classification » Diffusion model » Time series
