The Rise of Diffusion Models in Time-Series Forecasting
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要