面向多元概率时间序列预测的通道感知对比条件扩散模型
Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
AI总结：
本文提出通道感知对比条件扩散模型CCDM，通过通道中心去噪网络和基于去噪的时间对比学习提升多元概率时间序列预测精度与泛化能力，在基准测试中优于现有扩散预测器。
摘要：
从实际应用角度预测多元时间序列的可靠轨迹对于合理决策至关重要。近期方法主要定制生成式条件扩散模型来估计目标时间预测分布。然而，如何提高对给定隐式时间预测信息的利用效率以增强条件扩散学习，仍然是一个障碍。为此，我们提出了一种通用的通道感知对比条件扩散模型，命名为CCDM，以实现理想的多元概率预测，无需精心设计的时间条件归纳偏置。具体而言，我们首先设计了一个以通道为中心的条件去噪网络，用于管理变量内部变化和跨变量相关性，从而能够在多样的预测视界和通道数量上实现可扩展性。然后，我们设计了一种专门的基于去噪的时间对比学习，以显式放大过去观测与未来预测之间的预测互信息。它能够连贯地补充朴素的分步去噪扩散训练，并提高对未知测试时间序列的预测精度和泛化能力。此外，我们从神经互信息和时间分布泛化的角度，对这种辅助对比训练精化带来的益处提供了理论见解。所提出的CCDM在综合基准上展现出优于当前最先进扩散预测器的预测能力，在66.67%和83.33%的案例中取得了最佳的MSE和CRPS结果。我们的代码已公开于https://github.com/LSY-Cython/CCDM。
英文摘要：
Forecasting faithful trajectories of multivariate time series from practical scopes is essential for reasonable decision-making. Recent methods majorly tailor generative conditional diffusion models to estimate the target temporal predictive distribution. However, it remains an obstacle to enhance the exploitation efficiency of given implicit temporal predictive information to bolster conditional diffusion learning. To this end, we propose a generic channel-aware Contrastive Conditional Diffusion model entitled CCDM to achieve desirable Multivariate probabilistic forecasting, obviating the need for curated temporal conditioning inductive biases. In detail, we first design a channel-centric conditional denoising network to manage intra-variate variations and cross-variate correlations, which can lead to scalability on diverse prediction horizons and channel numbers. Then, we devise an ad-hoc denoising-based temporal contrastive learning to explicitly amplify the predictive mutual information between past observations and future forecasts. It can coherently complement naive step-wise denoising diffusion training and improve the forecasting accuracy and generality on unknown test time series. Besides, we offer theoretic insights on the benefits of such auxiliary contrastive training refinement from both neural mutual information and temporal distribution generalization aspects. The proposed CCDM can exhibit superior forecasting capability compared to current state-of-the-art diffusion forecasters over a comprehensive benchmark, with best MSE and CRPS outcomes on $66.67\%$ and $83.33\%$ cases. Our code is publicly available at https://github.com/LSY-Cython/CCDM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
