https://github.com/jrieke/timeseries-rnn
⏱️ char-rnn for time series data
https://github.com/jrieke/timeseries-rnn
char-rnn deep-learning generation machine-learning prediction stock-market time-series timeseries timeseries-rnn
Last synced: 2 months ago
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⏱️ char-rnn for time series data
- Host: GitHub
- URL: https://github.com/jrieke/timeseries-rnn
- Owner: jrieke
- License: mit
- Created: 2016-04-22T18:08:07.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-04-23T00:18:13.000Z (over 9 years ago)
- Last Synced: 2025-07-25T09:05:04.485Z (2 months ago)
- Topics: char-rnn, deep-learning, generation, machine-learning, prediction, stock-market, time-series, timeseries, timeseries-rnn
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 13
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# timeseries-rnn
These scripts implement a recurrent neural network, which can generate new data from existing time series. The idea is inspired by Andrej Karparthy's (@karparthy) [char-rnn](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), which does the same thing for text (see also his [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)). The network consists of one or more LSTM layers and a layer implementing a Gaussian mixture model (instead of the softmax in char-rnn). Therefore, the network predicts the parameters of a mixture distribution, from which the values of the time series at the next time step can be sampled. See pp. 9-10 in my report [here](https://github.com/jrieke/lstm-biology/blob/master/Project%20Report.pdf) for more details.
The code is based on [keras](http://keras.io/) and [Theano](http://deeplearning.net/software/theano/) (Note: The Tensorflow backend doesn't work right now because of a custom layer which is implemented in Theano).
## Usage
### Training
python train.py data/stocks
Train the network on all files in `data/stocks`. Each file should contain one time series and look like this:
1 2
3 4
5 6
Columns are variables (here: 2) and rows are time steps (here: 3). The data is automatically normalized to mean 0 and standard deviation 1. If you want to train on the change of the data from time step to time step (this is recommended for time series that continually in- or decrease, e.g. stock prices), use `--change`. The trained network is saved in `model`.
Note that the network architecture (namely the Gaussian mixture model layer) is quite prone to numeric errors. If you encounter a `nan` loss during training, try to reduce the learning rate (`--learning_rate VALUE`, default 0.001), use more standard deviations to normalize the data (`--stds VALUE`, default 1), or play around with the number of Gaussians in the mixture distribution (`--mixture_components VALUE`, default 10).
Arguments:
--epochs EPOCHS number of training epochs
--learning_rate LEARNING_RATE learning rate of RMSprop
--layers LAYERS number of LSTM layers
--neurons NEURONS number of neurons per LSTM layer
--mixture_components MIXTURE_COMPONENTS number of Gaussians in the mixture distribution
--change use change of data for training (good for cumulative sequences)
--stds STDS number of standard deviations to normalize the data with
### Prediction
python predict.py data/stocks
Use the saved model to predict the next values in the time series for all files in `data/stocks`. The predicted time series are stored in the format above in the directory `predicted`.
### Generation
python generate.py data/stocks
Use the saved model to generate completely new time series. Parts of the CSV files in `data/stocks` are used as seeds to initialize the network state. The generated time series are stored in the format above in the directory `predicted`.
## Requirements
- Python 2.7
- keras (v0.3.2)
- Theano (v0.8.0.dev0)
- numpy