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：扩散模型在跨领域生成建模任务中已取得最先进的性能。先前关于时间序列扩散模型的研究主要侧重于开发针对特定预测或插补任务的条件模型。本研究探索了任务无关的无条件扩散模型在若干时间序列应用中的潜力。我们提出TSDiff——一种面向时间序列的无条件训练扩散模型。所提出的自引导机制可在推理过程中对TSDiff进行条件化以适配下游任务，无需辅助网络或改变训练流程。我们在三个不同的时间序列任务上验证了该方法的效果：预测、改进与合成数据生成。首先，我们证明TSDiff与多种特定任务的预测方法具有竞争力（预测）。其次，我们利用TSDiff习得的隐式概率密度，以降低逆向扩散计算开销的方式迭代改进基础预测器的结果（改进）。值得注意的是，该模型的生成性能保持完整——基于TSDiff合成样本训练的下游预测器，其性能优于使用其他最先进时序生成模型样本训练的预测器，甚至在某些情况下超越基于真实数据训练的模型（合成）。