A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
arxiv(2024)
摘要
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.
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