Time-Series-Data-Augmentation-Generator

Generate augmented time series batches for sequence to sequence deep learning when training an LSTM/RNN.

This repo is for anyone training a sequence to seqeunce model that wants to use time series data augmentation on the fly It makes use of the awesome time series augmentation functions from tsaug library.

It expects data loaded into memory in this format: X_train: 3D array (N_sequences, sequence length, n_features) y_train: 3D array (N_sequences, sequence length, n_classes)

Usage

import seq2seq_ts_generator as tsaug_gen

training_gen = tsaug_gen.tsaug_generator(X_train, y_train, batch_size=128)
n = next(training_gen)
print(n)
hist = model.fit_generator(training_gen, epochs=100, 
                                       verbose=2, use_multiprocessing=True,
                           steps_per_epoch=16) 

It could easily be adapted for sequence to classification problems - just don't return augmented y - you don't need it.

Any Suggestions for improvements - e.g using tf.data to speed it up, then let me know.

Enjoy!