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arXiv · 2401.03006

The Rise of Diffusion Models in Time-Series Forecasting

Caspar Meijer·Lydia Y. Chen
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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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Caspar Meijer, Lydia Y. Chen. 2024-01-05. The Rise of Diffusion Models in Time-Series Forecasting. https://arxiv.org/abs/2401.03006

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