sktime-dl 0.1.0

A repository for off-the-shelf networks

The aim is to define Keras networks able to be directly used within sktime and its pipelining and strategy tools, and by extension scikit-learn, for use in applications and research. Overtime, we wish to interface or reimplement networks from the literature in the context of time series analysis.

Currently, we interface with a number of networks for time series classification in particular.

dl-4-tsc interfacing

This toolset currently serves as an interface to dl-4-tsc, and implements the following network archtiectures:

• Time convolutional neural network (CNN)

• Encoder (Encoder)

• Fully convolutional neural network (FCNN)

• Multi channel deep convolutional neural network (MCDCNN)

• Multi-scale convolutional neural network (MCNN)

• Multi layer perceptron (MLP)

• Residual network (resnet)

• Time Le-Net (tlenet)

• Time warping invariant echo state network (twiesn)

sktime-dl

James Large (@James-Large), Aaron Bostrom (@ABostrom), Hassan Ismail Fawaz (@hfawaz), Markus Löning (@mloning)

sktime

Project management: Jason Lines (@jasonlines), Franz Király (@fkiraly)

Design: Anthony Bagnall(@TonyBagnall), Sajaysurya Ganesh (@sajaysurya), Jason Lines (@jasonlines), Viktor Kazakov (@viktorkaz), Franz Király (@fkiraly), Markus Löning (@mloning)

Coding: Sajaysurya Ganesh (@sajaysurya), Bagnall(@TonyBagnall), Jason Lines (@jasonlines), George Oastler (@goastler), Viktor Kazakov (@viktorkaz), Markus Löning (@mloning)

We are actively looking for contributors. Please contact @fkiraly or @jasonlines for volunteering or information on paid opportunities, or simply raise an issue in the tracker.