👉 Season computing, also known as seasonal decomposition or seasonal time series analysis, is a statistical technique used to analyze and forecast time series data that exhibit regular patterns or cycles occurring at fixed intervals, typically yearly. This method breaks down a time series into three main components: trend (long-term progression), seasonality (repeating patterns within a fixed period), and residuals (irregular variations). By isolating these components, season computing helps in understanding the underlying structure of the data and improves the accuracy of predictions by accounting for seasonal fluctuations. This technique is widely used in various fields such as retail, agriculture, and finance to optimize decision-making processes based on historical data patterns.
