# ddz16/TSFpaper

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

Repository: https://github.com/ddz16/TSFpaper
Canonical: https://ross.abutalabs.com/products/tsfpaper
License Family: other
Topics: deep-learning, time-series, time-series-analysis, time-series-forecasting, time-series-prediction, deep-neural-networks, paper-lists, rnn, tcn, time-series-models, transformer, spatial-temporal-forecasting, spatio-temporal, spatio-temporal-data, spatio-temporal-prediction
Last push: 2026-08-14T03:20:46+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": 1526, "days_push": 19, "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 3191, forks 267 (observed 2026-08-28T04:07:48.183134+00:00)

## What it is
A curated awesome-list of 400+ research papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF), categorized by model type such as RNN, TCN, and Transformer. It covers univariate, multivariate, and spatio-temporal forecasting and is continuously updated with top-conference and arXiv papers.

## Use cases
- find papers on time series forecasting
- survey deep learning models for time series prediction
- research spatio-temporal forecasting for traffic or weather
- compare transformer vs RNN approaches for forecasting
- build a literature review for a forecasting thesis
- track latest arXiv papers on time series models

## When to choose
- you need a categorized reading list of forecasting research papers
- you are starting research on TSF or STF and want broad coverage
- you want to follow recent deep learning advances in time series

## When to avoid
- you need runnable code or a software library rather than papers
- you need classical statistical forecasting methods rather than deep learning
- you need a production forecasting tool

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

## Member repositories
- ddz16/TSFpaper (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:48.183134+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 2026-08-29T18:45:00.133566+00:00, confidence not recorded.
  - readme: https://github.com/ddz16/TSFpaper (fetched 2026-08-28T04:07:48.183134+00:00, sha f7eb6a12e59a)
- Data as of 2026-08-30T08:39:29.467469+00:00.