Time series decomposition can separate historical data into several components except for which of the following?

Time series decomposition is a technique used to break down historical data into its fundamental components, which typically include seasonal patterns, trend components, and irregular components.

Seasonal patterns account for regular fluctuations that occur at specific intervals, such as monthly or quarterly changes in sales. Trend components reflect the long-term progression of the data, indicating whether it is increasing or decreasing over time. Irregular components capture random variations that cannot be explained by the other components, often referred to as noise.

Horizontal patterns, on the other hand, do not constitute a recognized component of time series data in this context. While data might show periods of stability, this does not fall into the typical categories considered in time series decomposition. Analyzing horizontal patterns often requires different statistical methods or approaches than those used in decomposing time series, hence why this option does not align with the typical components analyzed in time series decomposition.