Seasonal decomposition

Seasonal decomposition is a method used to analyze time series data—like sales or temperatures—that change over time. It separates the data into different parts: the overall trend (long-term movement), seasonal patterns (regular fluctuations within each year or period, like holidays or seasons), and irregular or random variations. This helps identify underlying patterns and understand what influences the data at different times, making forecasting and decision-making more accurate. Essentially, it breaks down complex data into understandable components to reveal what’s driving changes over time.