In many machine learning applications, especially those involving time series data, obtaining a large, diverse, and high-quality labeled dataset is one of the biggest hurdles. Data collection is often constrained by availability, privacy, annotation costs, and time.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/betting-on-decomposition-based-time-series-augmentation