Seasonal Decomposition of Time Series

Seasonal decomposition of a time series is a method used to analyze data points collected over time by separating it into different components: the overall trend (long-term movement), seasonal patterns (regular fluctuations within a year, like holiday sales), and residuals (random noise or irregularities). This helps identify the underlying factors influencing the data, making it easier to forecast future values and understand patterns. Think of it as breaking down a complex song into its melody, rhythm, and background noise to better appreciate and analyze each part.