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Spatial-Temporal Denoising Diffusion Probabilistic Model for Industrial IoTs
Denoising Diffusion Probabilistic Models (DDPMs) are a highly popular class of deep generative models that have been successfully applied to various problems, including image and video generation. Recently, there have been attempts to apply these models to time-series data. However, they are not enough for modeling spatial-temporal interactions. They primarily focus on temporal dependencies within individual sensors, neglecting the spatial correlations between different nodes. In reality, objects are spatially correlated with each other. This project aims to explore a graph-based denoising diffusion probabilistic model to jointly capture both temporal dependencies and spatial interactions.
Keywords: Graph neural networks, Denoising diffusion probabilistic models, Generative AI, Multivariate time series, Internet of things
Requirements:
• Passionate about multivariate time-series data.
• Proficiency in PyTorch.
• Prior experiences in DDPM and Graph Neural Networks are a big plus.
• Currently enrolled as a Master’s student.
Requirements:
• Passionate about multivariate time-series data. • Proficiency in PyTorch. • Prior experiences in DDPM and Graph Neural Networks are a big plus. • Currently enrolled as a Master’s student.
This project seeks to merge the capabilities of DDPM with Spatial-Temporal Graph Neural Networks, potentially unlocking new possibilities for academic research and practical applications in the Industrial Internet of Things (IIoT). The objectives of this project include:
• Develop a graph-based denoising diffusion probabilistic model that effectively captures both temporal dependencies and spatial interactions in IIoT data.
• Conduct comprehensive experiments to compare the performance (e.g., forecasting, imputation, etc.) of the proposed model against existing state-of-the-art methods.
This project seeks to merge the capabilities of DDPM with Spatial-Temporal Graph Neural Networks, potentially unlocking new possibilities for academic research and practical applications in the Industrial Internet of Things (IIoT). The objectives of this project include:
• Develop a graph-based denoising diffusion probabilistic model that effectively captures both temporal dependencies and spatial interactions in IIoT data. • Conduct comprehensive experiments to compare the performance (e.g., forecasting, imputation, etc.) of the proposed model against existing state-of-the-art methods.
Graph-Based DDPM.pdf
