chenfeng-huang/STDiffusion-ICDM-2025
Official implementation for STDiffusion, published in ICDM 2025.
This project helps researchers, analysts, and engineers generate realistic, diverse, and interpretable multivariate time series data. You provide existing time series datasets (like energy consumption, stock prices, or weather data), and it creates new synthetic time series samples. This is useful for tasks like data augmentation, privacy-preserving data sharing, or exploring 'what-if' scenarios.
Use this if you need to generate high-quality, synthetic time series data for analysis, forecasting, or imputation, especially when interpretability and realism are crucial.
Not ideal if you only need to perform basic forecasting or imputation on existing data without generating new synthetic samples.
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Last pushed
Mar 09, 2026
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