The Rise of Diffusion Models in Time-Series Forecasting

• URL: http://arxiv.org/abs/2401.03006v2
• Date: Wed, 17 Jan 2024 14:02:12 GMT
• Title: The Rise of Diffusion Models in Time-Series Forecasting
• Authors: Caspar Meijer and Lydia Y. Chen
• Abstract summary: 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.
• Score: 5.808096811856718
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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