# lixus7/Time-Series-Works-Conferences

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

Repository: https://github.com/lixus7/Time-Series-Works-Conferences
Canonical: https://ross.abutalabs.com/products/time-series-works-conferences
License: MIT
License Family: permissive
Topics: time-series, deep-learning, spatio-temporal, paper-list, traffic-prediction, spatio-temporal-prediction, spatio-temporal-data, spatio-temporal-modeling, location, travel-time-prediction, demand-forecasting, anomaly-detection, multivariate-timeseries, probabilistic-models, time-series-forecasting, time-series-prediction, time-series-imputation, accident-detection
Last push: 2026-08-12T03:56:03+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 1623, "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 1040, forks 98 (observed 2026-08-28T04:03:20.151440+00:00)

## What it is
A curated summary of time-series research papers published at top CS conferences such as NeurIPS, ICML, ICLR, KDD, and AAAI. It organizes work on forecasting, imputation, anomaly detection, and spatio-temporal modeling for researchers and students.

## Use cases
- find recent time-series forecasting papers from top conferences
- survey spatio-temporal prediction research
- keep up with deep learning papers on time series
- research anomaly detection in multivariate time series
- find papers on traffic and travel-time prediction
- prepare a literature review for a time-series thesis

## When to choose
- you need a curated, conference-organized reading list for time-series research
- you want to track state-of-the-art spatio-temporal and forecasting models
- you are a student or researcher starting a literature survey

## When to avoid
- you need runnable code or a software library rather than a paper list
- you need non-academic tutorials or beginner courses on time series
- you need journals or preprints only, since coverage focuses on top conferences

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, data-science
- domain: time-series, deep-learning, machine-learning, tutorials, awesome-lists
- platform: -
- tags: paper-list, time-series-forecasting, spatio-temporal, anomaly-detection, traffic-prediction, academic-research, awesome-list, web-server

## Member repositories
- lixus7/Time-Series-Works-Conferences (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.151440+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 2026-08-30T07:03:30.508109+00:00, confidence not recorded.
  - readme: https://github.com/lixus7/Time-Series-Works-Conferences (fetched 2026-08-28T04:03:20.151440+00:00, sha 7ba704bf3b0a)
- Data as of 2026-08-30T08:39:29.467469+00:00.