Open Source Projects Supported by GC.OS

Feature engineering package with sklearn like functionality to engineer and select features for use in machine learning models.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

The framework for causal learning and probabilistic graphical models.

Prophet-like hierarchical forecasting and Marketing Mix Modeling algorithms.

The forecasting framework for state-of-the-art deep learning architectures.

Reference implementations of Kalman filters, Kalman smoothers, and EM algorithms.

A framework factory for AI toolboxes with scikit-learn-like and sktime-like parametric objects

Time series forecasting with machine learning models

The unified framework for probabilistic regression, survival/time-to-event prediction, and probability distributions.

The unified framework for AI with time series.