This file contains the core implementation of the DBA (Dynamic Time Warping Barycenter Averaging) algorithm in Python. It is self-contained, requiring only NumPy, and provides a function `performDBA` that computes the average of a set of time series under DTW. The algorithm first finds a medoid for initialization and then iteratively updates the average by aligning each series to the current average via DTW and updating each point as the mean of aligned values. This implementation demonstrates the non-obvious skill of efficiently implementing the iterative DTW-based averaging process.

This file extends the DBA algorithm to handle multivariate time series in a channels-first format (shape: [n_channels, length]). It adapts the core DTW and averaging steps to work with multi-dimensional sequences, maintaining the same iterative alignment and update process. This component is self-contained and demonstrates the non-obvious skill of adapting a univariate time series algorithm to the multivariate case while preserving the core properties of DBA.