tcn-ae¶

A temporal convolutional autoencoder (TCN-AE) for unsupervised anomaly detection in multivariate time series.

This package provides a reference implementation of the algorithm described in:

Markus Thill, Wolfgang Konen, Hao Wang, Thomas Bäck. Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 2021.

Not ready for use

The package currently contains no model implementation — it is packaging and infrastructure only, and installing it gives you nothing usable yet. The released versions exist to validate the release pipeline, not to be depended on. The TCN-AE implementation and a stable public API are still to come.

Installation¶

pip install tcn-ae

This installs Keras with the TensorFlow backend, matching the paper's original implementation.

Keras 3 is multi-backend, so PyTorch can be used instead:

pip install "tcn-ae[torch]"
KERAS_BACKEND=torch python your_script.py

TensorFlow is a required dependency rather than an extra, so tcn-ae[torch] installs both. To make the backend choice permanent, set "backend": "torch" in ~/.keras/keras.json.

There is one implementation, written against the Keras 3 API — the backend is chosen at install and run time, not by a separate code path.

Status¶

No model code yet. The installation instructions above describe how the package will be used once the implementation lands; today tcn_ae exposes only __version__.

See the API reference for what is currently available.