$ show yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model
This repository is a curated awesome list and survey companion for diffusion models applied to time series, spatio-temporal, and tabular data. It contains no executable code; the sole content is a large README.md that organizes hundreds of papers into a taxonomy by task (prediction, generation, imputation, anomaly detection, classification/regression, causal inference, event prediction, foundation models) and by application domain (healthcare, sequential recommendation, weather, energy, finance, AIOps, environment, traffic, manufacturing, audio, biology, communication). Each entry links to the paper and, where available, official code repositories. It is the companion to an ACM Computing Surveys paper (DOI 10.1145/3783986) and includes citation information, a Google Sheets taxonomy, and an overview figure. The repository is actively maintained with recent commits and welcomes pull requests. As a research resource, it is highly reproducible: all content is in the markdown file, with no dependencies, datasets, or environment setup required. Although classified as data_research due to its research orientation, it is not a data pipeline or ML codebase. Its value is as a reference index and literature map for researchers; the README itself is the reusable asset.
The LLM identified 1 files worth highlighting for reuse:
The entire repository is a single, well-organized Markdown file that categorizes hundreds of diffusion-model papers for time series, spatio-temporal, and tabular data. It is self-contained, requires no build or data, and can be reused as a reference taxonomy or starting point for literature reviews. It demonstrates curation skill through consistent formatting, task/domain organization, and links to official code.