# qingsongedu/awesome-AI-for-time-series-papers

A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.

Repository: https://github.com/qingsongedu/awesome-AI-for-time-series-papers
Canonical: https://ross.abutalabs.com/products/awesome-ai-for-time-series-papers
License: MIT
License Family: permissive
Topics: timeseries, timeseries-analysis, timeseries-forecasting, timeseries-prediction, timeseriesclassification, anomalydetection, changepoint-detection, classification, forecasting, missing-data, data-mining, deep-learning, machine-learning, signal-processing, awesome, awesome-list, tutorial, temporal-models, spatio-temporal-analysis, temporal-point-processes
Last push: 2024-04-06T21:11:38+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": 1553, "days_push": 879, "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 1623, forks 147 (observed 2026-08-28T04:05:12.641324+00:00)

## What it is
A curated awesome-list of papers, tutorials, and surveys on AI for Time Series Analysis from top AI conferences and journals, updated promptly as papers are accepted. It covers time series, spatio-temporal data, event data, and temporal point processes, with links to available code.

## Use cases
- find recent papers on time series forecasting
- survey deep learning methods for time series classification
- keep up with NeurIPS ICML ICLR time series papers
- find tutorials on spatio-temporal data analysis
- research anomaly detection in time series
- locate code implementations for time series papers

## When to choose
- you need a regularly updated reading list of top-venue time series research
- you are a researcher or engineer surveying AI4TS literature
- you want papers with available code links

## When to avoid
- you need runnable software or a library rather than a paper list
- you need preprints or non-top-venue papers only
- you need curated datasets or benchmarks

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, documentation
- domain: time-series, machine-learning, deep-learning, data-science, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, time-series-forecasting, anomaly-detection, papers, surveys, tutorials, spatio-temporal, research

## Member repositories
- qingsongedu/awesome-AI-for-time-series-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.641324+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 2026-08-30T03:48:53.646787+00:00, confidence not recorded.
  - readme: https://github.com/qingsongedu/awesome-AI-for-time-series-papers (fetched 2026-08-28T04:05:12.641324+00:00, sha d9ab41a9e746)
- Data as of 2026-08-30T08:39:29.467469+00:00.