Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks. In this work, we explore the potential of task-agnostic, unconditional diffusion models for several time series applications. We propose TSDiff, an unconditionally-trained diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. We demonstrate the effectiveness of our method on three different time series tasks: forecasting, refinement, and synthetic data generation. First, we show that TSDiff is competitive with several task-specific conditional forecasting methods (predict). Second, we leverage the learned implicit probability density of TSDiff to iteratively refine the predictions of base forecasters with reduced computational overhead over reverse diffusion (refine). Notably, the generative performance of the model remains intact -- downstream forecasters trained on synthetic samples from TSDiff outperform forecasters that are trained on samples from other state-of-the-art generative time series models, occasionally even outperforming models trained on real data (synthesize).

翻译：扩散模型已在各类生成建模任务中取得最先进性能。先前关于时间序列扩散模型的研究主要聚焦于为特定预测或插补任务开发条件模型。本文探索任务无关的无条件扩散模型在多个时间序列应用中的潜力。我们提出TSDiff——一种基于无条件训练的时间序列扩散模型。所提出的自引导机制可在推理过程中针对下游任务对TSDiff进行条件约束，无需辅助网络或改变训练流程。我们在三个不同时间序列任务中验证方法有效性：预测、精炼与合成数据生成。首先，我们证明TSDiff在预测任务中可媲美多个特定任务的条件预测方法（预测）。其次，我们利用TSDiff习得的隐式概率密度，通过降低反向扩散计算开销来迭代精炼基础预测器的预测结果（精炼）。值得注意的是，模型生成性能保持完好——基于TSDiff合成样本训练的下游预测器，其性能优于基于其他最先进时间序列生成模型样本训练的预测器，某些情况下甚至超越基于真实数据训练的模型（合成）。