Teaching AI to Classify Time-series Patterns with Synthetic Data - KDnuggets

However, we don't want to do a ton of feature engineering or learn complicated time-series algorithms (e.g. We just want to feed our time-series data (with proper labels) into some kind of supervised'learning' machine that can learn these categories (high or low variance, too few or too many anomalies, etc.) from the raw data. Why don't we take advantage of a Python library which can do this kind of classification for us automatically and all we have to do is to throw the data into it using standard Numpy/Pandas format? Even better if that library has the looks and feels of our favorite Scikit-learn package! We find such features in the beautiful library -- tslearn.