# jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

Repository: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction
Canonical: https://ross.abutalabs.com/products/lstm-neural-network-for-time-series-prediction
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2023-03-24T21:54:57+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": 3545, "days_push": 1258, "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 5215, forks 1961 (observed 2026-08-28T04:09:13.117843+00:00)

## What it is
A Keras/TensorFlow implementation of an LSTM neural network for predicting time series steps and sequences, with sine wave and stock market (S&P 500) example datasets. It accompanies an article and video tutorial explaining LSTM mechanics and usage.

## Use cases
- learn how LSTMs predict time series
- example code for sequence prediction with Keras
- forecast stock prices with an LSTM
- tutorial on LSTM deep neural networks
- starting point for time series forecasting projects

## When to choose
- you want a well-explained, minimal LSTM time series example with article and video support
- you need sample code and datasets (sine wave, stock data) to learn sequence prediction

## When to avoid
- you need a production-ready or maintained forecasting library
- you require recent TensorFlow/Keras versions - the code targets old versions (TF 1.10, Keras 2.2.2)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, time-series, tutorials
- platform: python
- tags: lstm, keras, time-series-forecasting, tensorflow, example-code

## Member repositories
- jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:13.117843+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 2026-08-29T18:00:11.053135+00:00, confidence not recorded.
  - readme: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction (fetched 2026-08-28T04:09:13.117843+00:00, sha ee3885708cca)
- Data as of 2026-08-30T08:39:29.467469+00:00.