Seasonal Decomposition: Understanding Trends and Seasonality

Introduction In the fascinating realm of data science , understanding the underlying patterns in time series data is crucial. One powerful technique for dissecting these patterns is seasonal decomposition . In this blog post, we’ll delve into the concept of seasonal decomposition, explore its components, and discuss how it can enhance our understanding of trends and seasonality. 1. What is Seasonal Decomposition? Seasonal decomposition is a method used to break down a time series into its fundamental components: Trend : The long-term movement or direction of the data. Seasonal : The repeating patterns or cycles that occur at fixed intervals (e.g., daily, monthly, or yearly). Residual (or Irregular) : The remaining noise or fluctuations after removing the trend and seasonal components. 2. Components of Seasonal Decomposition Let’s dive deeper into each component: 2.1 Trend The trend represents the overall behavior of the time series. It captures the gradual changes ove...