# qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM

A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.

Repository: https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM
Canonical: https://ross.abutalabs.com/products/awesome-timeseries-spatiotemporal-lm-llm
License Family: other
Topics: anomalydetection, autoscaling, deeplearning, forecasting, foundation-models, large-language-models, large-models, machinelearning, pre-training, rca, timeseries
Last push: 2024-12-22T08:19:51+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1181, "days_push": 619, "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 1222, forks 91 (observed 2026-08-28T04:04:02.385311+00:00)

## What it is
A curated awesome-list of papers, code, and datasets on Large Language Models and Foundation Models applied to time series, spatio-temporal, and event data. It accompanies a survey paper and is actively updated with new research resources.

## Use cases
- find papers on LLMs for time series forecasting
- survey foundation models for spatio-temporal data
- research time series anomaly detection with large models
- find datasets for multimodal time series analysis
- keep up with LLM4TS research
- prepare a literature review on time series foundation models

## When to choose
- you need a comprehensive, curated reading list on LLMs/foundation models for temporal data
- you are a researcher or student surveying this fast-moving field
- you want links to official code and datasets for cited papers

## When to avoid
- you need a runnable library or tool rather than a paper list
- you want production-ready forecasting code
- you need non-temporal LLM resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, data-science
- domain: time-series, large-language-models, artificial-intelligence, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, time-series-forecasting, foundation-models, spatiotemporal, research-papers, survey

## Member repositories
- qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:02.385311+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 2026-08-30T06:15:39.186633+00:00, confidence not recorded.
  - readme: https://github.com/qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM (fetched 2026-08-28T04:04:02.385311+00:00, sha 05e9a02a165e)
- Data as of 2026-08-30T08:39:29.467469+00:00.