# cuge1995/awesome-time-series

list of papers, code, and other resources

Repository: https://github.com/cuge1995/awesome-time-series
Canonical: https://ross.abutalabs.com/products/awesome-time-series
License Family: other
Topics: time-series, time-series-forecasting, time-series-prediction, series-forecasting, spatio-temporal
Last push: 2025-08-13T13:03:40+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 36, release rhythm 35, longevity 100
- inputs: {"age_days": 2374, "days_push": 385, "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 1062, forks 163 (observed 2026-08-28T04:03:26.088911+00:00)

## What it is
A curated awesome-list of state-of-the-art papers, code repositories, datasets, and competitions focused on time series forecasting. It covers topics from classic statistical methods to modern deep learning approaches including Transformers and Mamba-based models.

## Use cases
- find papers on time series forecasting
- learn state-of-the-art time series prediction methods
- find code implementations of forecasting models
- discover time series datasets
- prepare for time series forecasting competitions
- research spatio-temporal forecasting

## When to choose
- you need a curated reading list for time series forecasting research
- you want links to paper code implementations in one place
- you are looking for benchmark competitions and datasets

## When to avoid
- you need a working forecasting library rather than a resource list
- you need maintained, production-ready forecasting code

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: time-series, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, time-series-forecasting, papers, kaggle-competitions, spatio-temporal

## Member repositories
- cuge1995/awesome-time-series (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:26.088911+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 2026-08-30T06:56:43.973550+00:00, confidence not recorded.
  - readme: https://github.com/cuge1995/awesome-time-series (fetched 2026-08-28T04:03:26.088911+00:00, sha acb51cf0d8a2)
- Data as of 2026-08-30T08:39:29.467469+00:00.