# tgjeon/TensorFlow-Tutorials-for-Time-Series

TensorFlow Tutorial for Time Series Prediction

Repository: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series
Canonical: https://ross.abutalabs.com/products/tensorflow-tutorials-for-time-series
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow-tutorials, series-prediction, rnn
Last push: 2017-08-01T07:55:55+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3748, "days_push": 3319, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1101, forks 411 (observed 2026-08-28T04:03:35.565189+00:00)

## What it is
A collection of Jupyter notebook tutorials teaching time series prediction with TensorFlow using recurrent neural networks. It covers MNIST classification with RNNs, sine wave prediction with LSTM and Gaussian processes, and electricity price forecasting.

## Use cases
- learn RNN basics with TensorFlow
- predict sine wave with LSTM
- forecast electricity prices with neural networks
- classify MNIST using recurrent networks
- tutorial on time series prediction in TensorFlow

## When to choose
- learning RNN/LSTM concepts from worked notebooks
- studying classic TensorFlow r0.9-era code for historical reference

## When to avoid
- building production models on modern TensorFlow (code targets r0.9 and is unmaintained)
- needing up-to-date APIs or maintained examples

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning
- domain: machine-learning, tutorials, time-series
- platform: python
- tags: tensorflow, rnn, lstm, jupyter-notebook, time-series-forecasting, deprecated

## Member repositories
- tgjeon/TensorFlow-Tutorials-for-Time-Series (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.565189+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 2026-08-30T06:45:44.488644+00:00, confidence not recorded.
  - readme: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series (fetched 2026-08-28T04:03:35.565189+00:00, sha 5656e3989e20)
- Data as of 2026-08-30T08:39:29.467469+00:00.