Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point estimation performance compared to regression-based methods. This limitation stems from difficulties in providing sufficient contextual bias to track distribution shifts and in balancing output diversity with the stability and precision required for point forecasts. Existing diffusion-based approaches mainly focus on full-distribution modeling under probabilistic frameworks, often with likelihood maximization objectives, while paying little attention to dedicated strategies for high-accuracy point estimation. Moreover, other existing point prediction diffusion methods frequently rely on pre-trained or jointly trained mature models for contextual bias, sacrificing the generative flexibility of diffusion models. To address these challenges, we propose SimDiff, a single-stage, end-to-end framework. SimDiff employs a single unified Transformer network carefully tailored to serve as both denoiser and predictor, eliminating the need for external pre-trained or jointly trained regressors. It achieves state-of-the-art point estimation performance by leveraging intrinsic output diversity and improving mean squared error accuracy through multiple inference ensembling. Key innovations, including normalization independence and the median-of-means estimator, further enhance adaptability and stability. Extensive experiments demonstrate that SimDiff significantly outperforms existing methods in time series point forecasting.

翻译：扩散模型近期在时间序列预测领域展现出潜力，特别是在概率预测方面。然而，与基于回归的方法相比，它们往往难以达到最先进的点估计性能。这一局限性源于难以提供足够的上下文偏置以追踪分布漂移，以及在点预测所需的稳定性、精度与输出多样性之间取得平衡。现有的基于扩散的方法主要关注概率框架下的全分布建模，通常以似然最大化为目标，而很少关注针对高精度点估计的专门策略。此外，其他现有的点预测扩散方法常常依赖预训练或联合训练的成熟模型来提供上下文偏置，牺牲了扩散模型的生成灵活性。为解决这些挑战，我们提出了SimDiff，一个单阶段、端到端的框架。SimDiff采用一个精心设计的统一Transformer网络，同时作为去噪器和预测器，无需外部预训练或联合训练的回归器。它通过利用内在的输出多样性，并借助多次推理集成来提升均方误差精度，从而实现了最先进的点估计性能。包括归一化独立性和均值中位数估计器在内的关键创新进一步增强了模型的适应性和稳定性。大量实验表明，SimDiff在时间序列点预测任务上显著优于现有方法。