Seasonality

Seasonality refers to periodic fluctuations in time series data that occur at regular intervals, often influenced by external factors such as weather, holidays, or economic cycles. Mathematically, seasonality can be modeled using seasonal decomposition techniques, such as STL (Seasonal-Trend decomposition using Loess) or classical decomposition methods, which separate the time series into trend, seasonal, and residual components. The seasonal component is typically represented as a repeating pattern that can be quantified using Fourier series or seasonal indices. Identifying and modeling seasonality is crucial for accurate forecasting, as it allows for the adjustment of predictions based on expected periodic variations, thereby enhancing the performance of time series models.