用扩散模型降噪金融时间序列，提升预测与交易效率。
A Financial Time Series Denoiser Based on Diffusion Model
- 用条件扩散模型的正向反向过程逐步加噪去噪，重构原始数据。
- 去噪后数据在未来收益分类任务上性能显著提升。
- 可识别市场噪声状态，生成更优交易信号，减少交易成本。
金融时间序列常表现出低信噪比，给准确的数据解读、预测及决策带来重大挑战。生成模型因其模拟复杂数据模式的能力而受到关注，其中扩散模型尤为有效。本文提出一种新方法，利用扩散模型作为金融时间序列的去噪器，以提升数据可预测性与交易表现。通过条件扩散模型的前向与反向过程，逐步添加和移除噪声，实现从噪声输入中重建原始数据。大量实验表明，基于扩散模型去噪的时间序列在下游未来收益分类任务中表现显著提升。此外，由去噪数据生成的交易信号能带来更高利润且交易次数更少，从而降低交易成本并提高整体交易效率。最后，我们证明，使用在去噪时间序列上训练的分类器，可识别市场的噪声状态，并获得超额收益。
原文摘要 · Abstract (English)
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.
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