Metadata-Version: 2.1
Name: tire-cpd
Version: 0.1.2
Summary: This is an unsupervised change point detection toolbox, including time-invariant representation (TIRE) model with diamond loss, multi-channel time-invariant representation (MC-TIRE) model, and multi-view time-invariant representation (multiview TIRE) model.
License: MIT
Author: Zhenxiang Cao
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: matplotlib (==3.7.4)
Requires-Dist: numpy (==1.23.5)
Requires-Dist: pandas (==2.1.1)
Requires-Dist: scikit-learn (==1.4.2)
Requires-Dist: scipy (==1.10.1)
Requires-Dist: seaborn (==0.12.2)
Requires-Dist: tensorflow (==2.12.0)
Requires-Dist: tensorflow-probability (==0.20.0)
Description-Content-Type: text/markdown

TIRE-cpd toolbox 
===============================

Toolbox for time-invariant representation autoencoder approach (TIRE) for change point detection (CPD) task. Including three models: TIRE model with diamond loss [1], multi-channel TIRE model [2], and multi-view TIRE model [3].

The authors of these papers are:

- [Zhenxiang Cao](https://www.esat.kuleuven.be/stadius/person.php?id=2380) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)
- [Nick Seeuws](https://www.esat.kuleuven.be/stadius/person.php?id=2318) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)
- [Maarten De Vos](https://www.esat.kuleuven.be/stadius/person.php?id=203) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven and Dept. Development and Regeneration, KU Leuven)
- [Alexander Bertrand](https://www.esat.kuleuven.be/stadius/person.php?id=331) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)

All authors are affiliated to [LEUVEN.AI - KU Leuven Institute for AI](https://ai.kuleuven.be). 

** Use examples and function explanations can be found in GitHub repository: [tire-cpd_toolbox_example](https://github.com/caozhenxiang/tire-cpd_toolbox_examples). **

References
------------
[1] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2023. A novel loss for change point detection models with time-invariant representations. IEEE Signal Processing Letters, 30, pp.1737-1741.
[2] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2023. Change Point Detection in Multi-Channel Time Series Via a Time-Invariant Representation. IEEE Transactions on Knowledge and Data Engineering.
[3] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2024. A Multi-view Extension for Change Point Detection via Time-invariant Representations. Proceedings of EUSIPCO 2024.
