# yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model

A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).

Repository: https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model
Canonical: https://ross.abutalabs.com/products/awesome-timeseries-spatiotemporal-diffusion-model
Homepage: https://arxiv.org/abs/2404.18886
License Family: other
Last push: 2026-02-05T15:21:56+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 66, release rhythm 35, longevity 75
- inputs: {"age_days": 1059, "days_push": 209, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1005, forks 74 (observed 2026-09-01T02:14:08.648162+00:00)

## What it is
A curated awesome-list and survey repository cataloging diffusion models for time series, spatio-temporal, and tabular data, with papers, code, and applications. It accompanies an ACM Computing Surveys paper and is actively updated with community contributions.

## Use cases
- find papers on diffusion models for time series forecasting
- survey of generative models for spatio-temporal data
- literature review for time series diffusion research
- find code implementations of time series diffusion models
- learn about diffusion models for traffic or climate data
- start research on generative time series modeling

## When to choose
- you need a comprehensive, categorized reading list of diffusion model research for temporal data
- you want a peer-reviewed survey (ACM Computing Surveys) as a research foundation
- you want links to papers, code, and applications in one place

## When to avoid
- you need runnable software or a library rather than a paper list
- you need tutorials for beginners rather than research papers
- you need diffusion models for images or text rather than time series

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, time-series, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, diffusion-models, time-series, spatio-temporal, survey, paper-list, generative-models

## Member repositories
- yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:14:08.648162+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:13:40.875020+00:00, confidence not recorded.
  - readme: https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model (fetched 2026-09-01T02:14:08.648162+00:00, sha 01c21f39a3e6)
  - homepage: https://arxiv.org/abs/2404.18886 (fetched 2026-08-29T13:13:39.091219+00:00, sha 22a8b50eb144)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T13:13:39.100586+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T13:13:39.104909+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T13:13:39.106937+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T13:13:39.102717+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.