https://github.com/RobRomijnders/LSTM_tsc

An LSTM for time-series classification
https://github.com/RobRomijnders/LSTM_tsc

Last synced: about 1 month ago
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An LSTM for time-series classification

• Host: GitHub
• URL: https://github.com/RobRomijnders/LSTM_tsc
• Owner: RobRomijnders
• License: mit
• Created: 2016-04-05T06:24:27.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-12-15T18:27:30.000Z (over 6 years ago)
• Last Synced: 2024-02-10T05:44:00.027Z (5 months ago)
• Language: Python
• Size: 3.28 MB
• Stars: 405
• Watchers: 35
• Forks: 147
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Lists

• awesome_time_series_in_python - LSTM_tsc - series classification neural network| (Examples or singular models)
• awesome_time_series_in_python - LSTM_tsc - series classification neural network| (Examples or singular models)

README

        
### Update 10-April-2017

And now it works with Python3 and Tensorflow 1.1.0

### Update 02-Jan-2017

I updated this repo. Now it works with Tensorflow 0.12. In this readme I comment on some new benchmarks

### LSTM for time-series classification

This post implements a Long Short-term memory for time series classification(LSTM). An LSTM is the extension of the classical Recurrent Neural Network. It has more flexibility and interpretable features such as a memory it can read, write and forget.

## Aim

This repo aims to show the minimal Tensorflow code for proper time series classification. The main function loads the data and iterates over training steps. The *tsc_model.py* scripts contains the actual model.

This repo contrasts with [another project](http://robromijnders.github.io/CNN_tsc/) where I implement a similar script using convolutional neural networks as the model

## Data and results

The code generalizes for any of the [UCR time series](http://www.cs.ucr.edu/~eamonn/time_series_data/). With the parameter *dataset* you can run the code on any of their datasets.

For your interests, you may compare performances with the nice overview in [this paper.](https://arxiv.org/pdf/1603.06995v4.pdf) They benchmark their CNN and other models on many of the UCR time series datasets

This code works amongst others for

  * __Two_Patterns__ where it achieves state-of-the-art, bein 100% test accuracy

  * __ChlorineConcentration__ where it achieves state-of-the-art, being 80% test accuracy

# Credits

Credits for this project go to [Tensorflow](https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html#recurrent-neural-networks) for providing a strong example, the [UCR archive](http://www.cs.ucr.edu/~eamonn/time_series_data/) for the dataset and my friend Ryan for strong feedback.

License is in `LICENSE.txt`

As always, I am curious to any comments and questions. Reach me at [email protected]