# Alro10/deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

Repository: https://github.com/Alro10/deep-learning-time-series
Canonical: https://ross.abutalabs.com/products/deep-learning-time-series
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: time-series-forecasting, lstm-neural-networks, lstm, deep-learning, forecasting-models, prediction, python3, pytorch, tensorflow, series-forecasting, series-classification, time-series, time-series-prediction, time-series-classification, forecasting-competitions, demand-forecasting, recurrent-neural-networks, series-analysis, sales-forecasting, deep-neural-networks
Last push: 2024-03-16T23:52:38+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": 2568, "days_push": 900, "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 2781, forks 527 (observed 2026-08-28T04:07:21.706489+00:00)

## What it is
A curated list of state-of-the-art papers, code, experiments, competitions, and datasets on deep learning for time series forecasting. It covers architectures like LSTM, Transformers, N-BEATS, and diffusion models with links to implementations.

## Use cases
- find papers on deep learning time series forecasting
- learn LSTM models for sales forecasting
- compare classic vs deep learning forecasting methods
- find code implementations of forecasting papers like Informer and Autoformer
- discover time series forecasting competitions and datasets
- study demand forecasting with neural networks

## When to choose
- you want a curated reading list of SOTA forecasting papers with code links
- you are researching deep learning approaches to time series prediction
- you need references for forecasting competitions or datasets

## When to avoid
- you need a production-ready forecasting library rather than a resource list
- you want classical statistical methods like ARIMA without deep learning
- you expect maintained, runnable software

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning
- domain: time-series, machine-learning, tutorials
- platform: python
- tags: time-series-forecasting, curated-list, papers, lstm, transformers, pytorch, tensorflow, awesome-list

## Member repositories
- Alro10/deep-learning-time-series (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:21.706489+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 2026-08-30T08:16:05.808700+00:00, confidence not recorded.
  - readme: https://github.com/Alro10/deep-learning-time-series (fetched 2026-08-28T04:07:21.706489+00:00, sha 362f604e923c)
- Data as of 2026-08-30T08:39:29.467469+00:00.