Abstract
The study of time series data is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a dynamic perspective on complex system interactions. Recently, diffusion models have seen widespread application in time series and spatio-temporal data mining. Not only do they enhance the generative and inferential capabilities for sequential and temporal data, but they also extend to other downstream tasks. In this survey, we comprehensively and thoroughly review the use of diffusion models in time series and spatio-temporal data, categorizing them by model category, task type, data modality, and practical application domain. In detail, we categorize diffusion models into unconditioned and conditioned types and discuss time series data and spatio-temporal data separately. Unconditioned models, which operate unsupervised, are subdivided into probability-based and score-based models, serving predictive and generative tasks such as forecasting, anomaly detection, classification, and imputation. Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks. Our survey extensively covers their application in various fields, including healthcare, recommendation, climate, energy, audio, and transportation, providing a foundational understanding of how these models analyze and generate data. Through this structured overview, we aim to provide researchers and practitioners with a comprehensive understanding of diffusion models for time series and spatio-temporal data analysis, aiming to direct future innovations and applications by addressing traditional challenges and exploring innovative solutions within the diffusion model framework.
Abstract (translated)
研究时间序列数据对于理解随时间变化的趋势和异常现象,以及跨各种行业的预测性见解至关重要。而空间-时间数据则对于分析空间和时间现象,以及复杂系统交互的动态视角具有至关重要的作用。最近,扩散模型在时间序列和空间-时间数据挖掘中得到了广泛应用。不仅它们能够增强序列和时间数据的生成和推断能力,而且它们还扩展到其他下游任务。在本次调查中,我们全面、深入地回顾了扩散模型在时间序列和空间-时间数据中的应用,并将它们按模型分类、任务类型、数据模态和实践应用领域进行分类。详细来说,我们将扩散模型分为有条件和支持性两种类型,并分别讨论时间序列数据和空间-时间数据。有条件模型 unsupervised 模型进一步细分为基于概率和基于评分的模型,为预测、异常检测、分类和反向工程等生成和推断任务提供支持。支持性模型另一方面则利用额外的信息来提高性能,并为预测和生成任务同样细分为有条件和无条件。我们的调查详细涵盖了它们在各个领域中的应用,包括医疗、推荐、气候、能源、音频和交通等,为研究人员和实践者提供了对扩散模型在时间序列和空间-时间数据分析方面的全面了解,旨在通过解决传统挑战和探索扩散模型框架内的创新解决方案,引导未来的创新和发展。
URL
https://arxiv.org/abs/2404.18886
