# qingsongedu/time-series-transformers-review

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Repository: https://github.com/qingsongedu/time-series-transformers-review
Canonical: https://ross.abutalabs.com/products/time-series-transformers-review
License: MIT
License Family: permissive
Topics: timeseries, transformer, forecasting, anomalydetection, classification, timeseries-analysis, time-series, time-series-forecasting, machine-learning, deep-learning, awesome, survey, transformers, review
Last push: 2024-08-08T15:03:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1647, "days_push": 755, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3002, forks 270 (observed 2026-08-28T04:07:37.490657+00:00)

## What it is
A curated awesome-list repository collecting papers, code, and datasets on Transformers applied to time series, accompanying the IJCAI'23 survey 'Transformers in Time Series: A Survey'. It organizes resources by taxonomy and application domains such as forecasting, anomaly detection, and classification.

## Use cases
- find papers on transformers for time series forecasting
- survey transformer models for anomaly detection in time series
- locate official code implementations of time series transformer models
- get started with deep learning research on time series
- find datasets for time series transformer benchmarks
- cite a comprehensive survey of transformers in time series

## When to choose
- you need a curated reading list of time series transformer research
- you are writing a literature review on transformer-based forecasting or anomaly detection
- you want links to official code for recent time series transformer papers

## When to avoid
- you need a runnable library or framework rather than a resource list
- you need non-transformer time series models
- you need maintained, tested software for production forecasting

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, machine-learning, deep-learning
- domain: time-series, deep-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, survey, transformers, forecasting, anomaly-detection, time-series-classification, papers, curated-resources

## Member repositories
- qingsongedu/time-series-transformers-review (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.490657+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 2026-08-29T18:47:25.075025+00:00, confidence not recorded.
  - readme: https://github.com/qingsongedu/time-series-transformers-review (fetched 2026-08-28T04:07:37.490657+00:00, sha 64bcc58448dd)
- Data as of 2026-08-30T08:39:29.467469+00:00.