Mis on ajajärjeste dekompositsioon trendi ja hooajalisuse järgi?

The decomposition of time series is the process of breaking down a time series into several components, typically into trend, seasonal, and residual (noise).

• Trend reflects the long-term direction of data change (e.g., increase in sales over several years).
• Seasonality are recurring cycles or patterns that occur with a certain periodicity (e.g., increase in sales during summer each year).

Decomposition helps to better understand the structure of data and improve forecasting. For example, one can isolate the trend to see the overall direction, and seasonality to account for recurring fluctuations.

Example: if you have monthly sales data over several years, decomposition will allow you to identify:

• An increase or decrease in sales (trend)
• Annual fluctuations (seasonality)
• Random fluctuations (residuals)

Decomposition methods include classical additive or multiplicative models, STL (Seasonal-Trend decomposition using Loess), and others.