ts-embedder's Introduction

Time-Series Embedder

Representing time-series is not straightforward. When deep learning solutions are considered, the number of samples plays a critical role, due to overfitting.

This project aims to provide an umbrella for time-series embedding solutions. First target is to implement the basic/naive approaches. Then, shape them to overcome overfitting.

Software Engineering Aspects

Keeps a definite interface for generators (the so-called inner-generator) feeding the models.
- Users may provide their custom generators (the so-called outer-generator) to integrate any data source.
- Thus, we separate the ingestion logic from ML/DL architectures/models.
Abstracts shared training and embedding tasks so that developers can focus only on the model architectures.
Currently supports 2 main approaches:
- Seq2seq learning
  - Re-usable and generic encoder-decoders allowing choice of recurrent cells, their parameters; the number of layers in encoder and decoder etc.
- VAE learning
  - Enables both continuous and discrete/concerete latent embeddings.
  - Enables users to define encoder and decoder over a config file for high flexiblity.
Provides discriminative wrappers for both approaches.
Provides basic processors and layers in addition to the ones in Keras.
Hopefully, this software engineering approach will yield a better maintainable and flexible solution.

TO-DO

Although informal tests are conducted, we need more formal tests.
Please, be careful with loss usage. Do not use before analyzing the needs, tensor shapes and theory.
Need to benchmark!
- On small number of samples, it tends to overfit.
- Any contribution is kindly welcomed for applying the solution on large number of samples.
May add existing time-series embedding solutions.

Recommend Projects