An LSTM for time-series classification
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ChlorineConcentration
results
.gitignore
README.md
_config.yml
tsc_main.py
tsc_model.py

README.md

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 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. 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. 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 for providing a strong example, the UCR archive for the dataset and my friend Ryan for strong feedback.

As always, I am curious to any comments and questions. Reach me at romijndersrob@gmail.com