🤖 AI Summary
Existing research on diffusion models for time-series and spatio-temporal data lacks a unified modeling framework and systematic task-specific analysis. Method: We propose the first multidimensional taxonomy tailored to temporal/spatio-temporal diffusion modeling, structured along three axes: model architecture (e.g., unconditional probabilistic vs. score-based models, conditional injection mechanisms), task paradigms (forecasting, generation, imputation, anomaly detection), and application domains (healthcare, climate, transportation, etc.). Contribution/Results: We construct a knowledge graph spanning six domains and deliver a reusable model selection guideline; explicitly characterize how conditional modeling enhances downstream task performance; and integrate key techniques—including diffusion process design, spatio-temporal graph neural networks, and multimodal fusion. This work establishes theoretical foundations and a technical roadmap for dynamic data modeling, advancing the paradigm shift of diffusion models toward time-series intelligence.
📝 Abstract
The study of time series 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 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.