2.3.6

• I have transitioned from setup.py to pyproject.toml . #89
• Changed the layout from flat to source. See here for advantages
• Updated the installation description in the README.md
• Fixed a couple of deprecation and future warnings, reported in #91

2.3.5

Replaces 2.3.4 due to differences between github and pypi

2.3.4

• Extend the reporting if time series tolerances are exceeded and add the option to silence them with a tolerance value.

• set default tolerance value to 1e-13

Version 2.3.3

Drop support for Python<3.9 and fix some depreciation warnings.

Version 2.3.2

The pandas version is limited to be less than 3.0 as, some commands will be deprecated in the requirements. a new release in the setup, 2.3.2, and warning silence. Deprecation warnings are silenced.

Version 2.3.1

• accelerate rescale cluster periods
• update documentation and include autodeployment

Version 2.3.0

• Fix depreciated pandas functions
• fix sum for distribution representation incl. min max vals - now mean value of representation equals mean value of original time series
• add possibility to define segment representation
• extend the default example
• switch from travis to github workflow for ci

Version 2.2.2

• Fix Hypertuning class
• Set high as new default MILP solver
• Rework README

Version 2.1.0

Following functionality was added:

• a hyperparameter tuning meta class which is able to identify the optimal combination of typical periods and segments for a given time series dataset

Version 2.0.1

tsam release (2.0.1) includes the following new functionalities:

• Changed dependency of scikit-learn to make tsam conda-forge runnable.