Seasonality is removed from a time series to isolate underlying trends or irregular fluctuations. This process typically involves identifying and subtracting the repeating seasonal patterns from the original data. Common methods include differencing, moving averages, and decomposition techniques. The goal is to transform the data into a stationary form, making it easier to analyze or model non-seasonal components like trends or anomalies.
One straightforward approach is seasonal differencing, where the value from the same period in the previous seasonal cycle is subtracted. For example, in monthly sales data with a yearly pattern, subtracting each month’s value from its counterpart 12 months earlier removes the annual seasonality. Another method is decomposition, which splits the time series into trend, seasonal, and residual components. Additive decomposition (trend + seasonality + noise) works when seasonal variations are constant, while multiplicative decomposition (trend * seasonality * noise) is better for varying magnitudes. Tools like Python’s statsmodels library provide built-in functions (e.g., seasonal_decompose()) to automate this. For instance, decomposing quarterly revenue data might reveal a consistent holiday sales spike, which can then be subtracted to focus on underlying growth.
Developers can also use model-based methods like SARIMA (Seasonal AutoRegressive Integrated Moving Average), which explicitly incorporates seasonal parameters. For example, SARIMA(1,1,1)(1,1,1,12) includes seasonal autoregressive and moving average terms for a 12-month cycle. Alternatively, regression models with dummy variables (e.g., one-hot encoded months) can capture and remove seasonal effects. In practice, a retail company might use Python’s pmdarima library to fit a SARIMA model, then extract residuals to analyze non-seasonal trends. Key considerations include testing for stationarity (e.g., with Augmented Dickey-Fuller tests) and validating whether seasonality is additive or multiplicative. Choosing the right method depends on data characteristics and the analysis goal, but starting with simple differencing or decomposition often provides a clear path.