Handling noisy data and outliers

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.