Time Series Decomposition
Time series decomposition is a statistical method used to break down a time series data set into its individual components, typically trend, seasonal, and irregular variations. Trend represents the long-term movement of the data, seasonal variation captures recurring patterns within a specific time frame, and irregular variation accounts for random fluctuations in the data that cannot be attributed to trend or seasonality. By decomposing a time series, analysts can better understand the underlying patterns and make more accurate forecasts or predictions.