How do seasonal and irregular components with values greater than 1.00 relate to the trend estimate?

In time series analysis, seasonal and irregular components can significantly influence the observed data in relation to its underlying trend. When these components have values greater than 1.00, it indicates that they are exerting a positive effect on the data relative to the trend estimate.

Specifically, a seasonal component shows patterns that repeat over a specific period (like sales increase during holidays), while irregular components reflect unpredictable fluctuations (like a sudden spike in demand). When their values exceed 1.00, it suggests that these effects are amplifying the data compared to what the trend would suggest. Therefore, a component value greater than 1.00 indicates performance or behavior that is above the trend estimate.

This understanding is critical for businesses and analysts as it helps them to forecast future values more accurately, by distinguishing between fluctuations that are expected due to seasonal effects and those that may arise unexpectedly. Recognizing when components are above the trend allows for better decision-making, inventory planning, and resource allocation.