A Financial Time Series Denoiser Based on Diffusion Model

ArXiv ID: 2409.02138 “View on arXiv”

Authors: Unknown

Abstract

Financial time series often exhibit low signal-to-noise ratio, posing significant challenges for accurate data interpretation and prediction and ultimately decision making. Generative models have gained attention as powerful tools for simulating and predicting intricate data patterns, with the diffusion model emerging as a particularly effective method. This paper introduces a novel approach utilizing the diffusion model as a denoiser for financial time series in order to improve data predictability and trading performance. By leveraging the forward and reverse processes of the conditional diffusion model to add and remove noise progressively, we reconstruct original data from noisy inputs. Our extensive experiments demonstrate that diffusion model-based denoised time series significantly enhance the performance on downstream future return classification tasks. Moreover, trading signals derived from the denoised data yield more profitable trades with fewer transactions, thereby minimizing transaction costs and increasing overall trading efficiency. Finally, we show that by using classifiers trained on denoised time series, we can recognize the noising state of the market and obtain excess return.

Keywords: diffusion model, time series denoising, financial forecasting, generative models, return classification, Quantitative Finance

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper is mathematically dense, leveraging advanced concepts from generative modeling (diffusion models, score matching, SDEs) and deriving specific objectives, while the empirical component is strong, featuring extensive experiments on real-world financial data with backtest-ready trading signals and reported performance metrics.

  flowchart TD
    A["Research Goal: Denoise Financial Time Series<br/>to Improve Predictability & Trading Performance"] --> B["Methodology: Conditional Diffusion Model"]
    B --> C["Input: Noisy Financial Time Series Data"]
    C --> D["Forward Process: Add Noise<br/>to create diffusion steps"]
    D --> E["Reverse Process: Denoise & Reconstruct<br/>to restore clean signal"]
    E --> F["Computational Process:<br/>Generate Denoised Time Series"]
    F --> G["Key Findings / Outcomes"]
    G --> G1["Enhanced Future Return<br/>Classification Performance"]
    G --> G2["Profitable Trading Signals with<br/>Fewer Transactions (Lower Cost)"]
    G --> G3["Classifier identifies Market Noise State<br/>to obtain Excess Return"]