Utility functions for working with time series data in Python. Functionality is primarily oriented around converting time series data into a format suitable for prediction with methods for tabular data such as gradient boosting. This involves "featurizing" the data and creating columns with the prediction target(s).
We use conda for environment management and PDM for dependency management. With these tools, the development environment can be set up using:
conda create -n timeseriesutils python=3.11
conda activate timeseriesutils
pdm sync