# TSCenter/awesome-time-series-papers

An Awesome List of the latest time series papers and code from top AI venues.

Repository: https://github.com/TSCenter/awesome-time-series-papers
Canonical: https://ross.abutalabs.com/products/awesome-time-series-papers
License: GPL-3.0
License Family: copyleft
Topics: anomaly-detection, time-series, time-series-forecasting, causal-discovery, time-series-analysis, early-detection, irregular, irregular-time-series, time-series-classification, time-series-early-classification, time-series-models, causal-discovery-methods, time-series-foundation-model
Last push: 2026-08-12T17:00:18+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 54
- inputs: {"age_days": 765, "days_push": 21, "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 1089, forks 54 (observed 2026-08-28T04:03:32.723767+00:00)

## What it is
A curated awesome list of recent time series research papers and code from top AI venues like NeurIPS, ICML, KDD, and ICLR. It covers forecasting, anomaly detection, classification, irregular time series, causal discovery, and time series foundation models.

## Use cases
- find recent papers on time series forecasting
- survey time series anomaly detection research
- locate code implementations for time series papers
- keep up with time series papers from top AI conferences
- learn about time series foundation models
- find research on irregular time series learning
- discover causal discovery methods for time series

## When to choose
- you need a regularly updated reading list of time series research
- you are writing a literature review or survey on time series topics
- you want paper-to-code links for reproducing experiments

## When to avoid
- you need a runnable library or benchmark framework rather than a paper list
- you need tutorials or courses rather than research paper references

## 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, anomaly-detection, causal-discovery, research-papers, time-series-classification, foundation-models

## Member repositories
- TSCenter/awesome-time-series-papers (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.723767+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 2026-08-30T06:49:16.612552+00:00, confidence not recorded.
  - readme: https://github.com/TSCenter/awesome-time-series-papers (fetched 2026-08-28T04:03:32.723767+00:00, sha d93fa8a256dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.