Seasonal decomposition is a tool that breaks down data into three components: trend, seasonality and residual. Trend represents the long-term direction or movement in the data over time. Seasonality represents the repeating short-term patterns or cycles that occur at regular intervals. Further, noise or the residuals represent the random variations or irregular fluctuations in the data that cannot be attributed to trend or seasonality.

Seasonal decomposition improves forecasting by isolating seasonality and trend that makes data easier to model using techniques such as ARIMA or LSTM. Removal of seasonality and trends in the data highlights anomalies, leading to effective noise management. For instance; seasonality affects the trends in the data and complexity arises. Decomposition enables to treat each component separately. Predicatable seasonal fluctuations such as increased retail sales during holidays should be eliminated to draw the true state of market or economic activity.

Differencing and detrending are two commonly used techniques that helps to isolate non-seasonal component of the data to generate clearer understanding of the trends and cycles.