This survey delves into the application of diffusion models in time-series forecasting. Diffusion models are demonstrating state-of-the-art results in various fields of generative AI. The paper includes comprehensive background information on diffusion models, detailing their conditioning methods and reviewing their use in time-series forecasting. The analysis covers 11 specific time-series implementations, the intuition and theory behind them, the effectiveness on different datasets, and a comparison among each other. Key contributions of this work are the thorough exploration of diffusion models' applications in time-series forecasting and a chronologically ordered overview of these models. Additionally, the paper offers an insightful discussion on the current state-of-the-art in this domain and outlines potential future research directions. This serves as a valuable resource for researchers in AI and time-series analysis, offering a clear view of the latest advancements and future potential of diffusion models.

翻译：本综述深入探讨了扩散模型在时间序列预测中的应用。扩散模型在生成式人工智能的多个领域展现出最先进的性能。本文提供了关于扩散模型的全面背景知识，详细阐述了其条件化方法，并回顾了它们在时间序列预测中的使用。分析涵盖了11种具体的时间序列实现方法、其背后的直觉与理论、在不同数据集上的有效性以及相互之间的比较。本研究的主要贡献在于对扩散模型在时间序列预测中的应用进行了深入探索，并按照时间顺序概述了这些模型。此外，本文还对该领域的当前最新技术进行了富有洞见的讨论，并指出了未来潜在的研究方向。这为人工智能和时间序列分析领域的研究人员提供了宝贵资源，清晰展示了扩散模型的最新进展与未来潜力。