Self-attention-based Diffusion Model for Time-series Imputation

Time-series modeling is essential for applications in agriculture, weather forecasting, food production, and more. However, missing data due to sensor malfunctions, power outages, and human errors is a common issue, complicating the training of machine learning models. We propose a diffusion-based generative model to

address this problem and fill the gaps in the data. Our approach captures feature and time correlations through a two-stage imputation process. Our model outperforms state-of-the-art imputation methods and is more scalable in GPU resources.