Tsam

Latest version: v2.3.1

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2.3.1

* accelerate rescale cluster periods
* update documentation and include autodeployment

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

2.2.2

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

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

2.0.1

tsam release (2.0.1) includes the following new functionalities:

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

2.0.0

In tsam’s latest release (2.0.0) the following functionalities were included:

* A new comprehensive structure that allows for free cross-combination of clustering algorithms and cluster representations, e.g. centroids or medoids.
* A novel cluster representation method that precisely replicates the original time series value distribution in the aggregated time series based on “Hoffmann, Kotzur and Stolten (2021): The Pareto-Optimal Temporal Aggregation of Energy System Models (https://arxiv.org/abs/2111.12072)”
* Maxoids as representation algorithm which represents time series by outliers only based on “Sifa and Bauckhage (2017): Online k-Maxoids clustering”
* K-medoids contiguity: An algorithm based on “Oehrlein and Hauner (2017): A cutting-plane method for adjacency-constrained spatial aggregation” that accounts for contiguity constraints to e.g. cluster only time series in neighboring regions

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