# yangwohenmai/LSTM

基于LSTM的时间序列预测研究

Repository: https://github.com/yangwohenmai/LSTM
Canonical: https://ross.abutalabs.com/products/lstm
Language: Python
License Family: other
Topics: forecast, lstm
Last push: 2022-12-14T02:47:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2872, "days_push": 1358, "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 3678, forks 785 (observed 2026-08-28T04:08:13.709617+00:00)

## What it is
A Chinese-language tutorial collection of LSTM neural network examples for time series forecasting, built on TensorFlow and Keras. It covers data cleaning, feature extraction, modeling, and prediction through single-variable, multivariable, and multi-step forecasting examples.

## Use cases
- learn LSTM for time series forecasting
- predict sales with LSTM in Keras
- multivariate time series prediction example
- multi-step forecasting with neural networks
- prepare time series data for supervised learning
- stock price prediction with LSTM

## When to choose
- you want worked, step-by-step LSTM forecasting examples in Python
- you are learning TensorFlow/Keras for sequence prediction
- you need reference code for data preprocessing of time series

## When to avoid
- you need a production-ready forecasting library
- you want maintained software with a license
- you need frameworks other than TensorFlow/Keras

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: time-series, machine-learning, deep-learning, tutorials
- platform: python
- tags: lstm, time-series-forecasting, keras, tensorflow, chinese-documentation, tutorial-notebooks

## Member repositories
- yangwohenmai/LSTM (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.709617+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 2026-08-29T18:31:15.069728+00:00, confidence not recorded.
  - readme: https://github.com/yangwohenmai/LSTM (fetched 2026-08-28T04:08:13.709617+00:00, sha 655a8fb1b06c)
- Data as of 2026-08-30T08:39:29.467469+00:00.