Handling noisy data and outliers

Use the Smooth Data activity to replace noisy data and outliers with an approximate function.

Eliminating random variation in data and allowing important patterns to stand out is important before training the model. Smoothing techniques create an approximate function that replaces noisy data and outliers. These methods are applied to reveal underlying trends in time series analysis, forecasting, seasonal, and cyclic components.