Traditional time series analysis separates preprocessing tasks, such as computing lags, applying transformations, and feature engineering, from the actual model fitting process, making the overall workflow more cumbersome.
In contrast, MLForecast integrates preprocessing tasks within a single class. This streamlines the workflow, enabling easy experimentation with different preprocessing and modeling combinations.