用布朗桥降低随机性，提升时间序列预测精度
Series-to-Series Diffusion Bridge Model
- 基于布朗桥设计逆过程，减少预测中的随机波动
- 在点对点预测上超越现有扩散模型，概率预测也具竞争力
- 适合需要高精度确定性预测的研究与工业场景
扩散模型在时间序列预测中表现突出，能有效建模复杂数据分布。然而，其固有的随机性常导致确定性预测不稳定。本文重新审视时间序列扩散模型，构建涵盖主流方法的统一框架。在此基础上，提出新型扩散时间序列预测模型S²DBM，利用布朗桥过程降低反向生成的随机性，并通过历史数据引入有信息量的先验和条件，提升预测准确性。实验表明，S²DBM在点对点预测中表现优异，且在概率预测任务上与其它扩散模型相当。
原文摘要 · Abstract (English)
Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in deterministic predictions is often constrained by instability arising from their inherent stochasticity. In this paper, we revisit time series diffusion models and present a comprehensive framework that encompasses most existing diffusion-based methods. Building on this theoretical foundation, we propose a novel diffusion-based time series forecasting model, the Series-to-Series Diffusion Bridge Model ($\mathrm{S^2DBM}$), which leverages the Brownian Bridge process to reduce randomness in reverse estimations and improves accuracy by incorporating informative priors and conditions derived from historical time series data. Experimental results demonstrate that $\mathrm{S^2DBM}$ delivers superior performance in point-to-point forecasting and competes effectively with other diffusion-based models in probabilistic forecasting.
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