Solution

Time series decomposition involves breaking down a time series into three main components: 1. Trend: The long-term direction or movement in the data (e.g., increasing or decreasing). 2. Seasonality: The repeating fluctuations at regular intervals due to seasonal factors (e.g., monthly, quarterly). 3. Residuals (or irregular components): The random, unexplained fluctuations that remain after removing the trend and seasonality. These are the "noise" or errors in the data. This decomposition is critical because it allows for better understanding and forecasting by isolating predictable patterns from random variations. Option A is incorrect because correlation is not a component of time series decomposition. Option B is incorrect as the cyclical component is different from seasonality and is often linked to economic cycles, not necessarily regular time intervals. Option C is incorrect because "cyclic behavior" and "random noise" are not formal components in standard time series decomposition. Option E is incorrect because outliers and forecast errors are not part of the standard decomposition process.