timesseasonal

Timesseasonal is a term used in time series analysis to describe the recurring seasonal component of a time-stamped dataset. It refers to predictable fluctuations that repeat over a fixed cycle, such as monthly sales cycles, weekly demand patterns, or intraday electricity usage. Timesseasonal patterns can coexist with long-term trends and irregular fluctuations, and their amplitude and timing may change across years or in response to holidays and events.

Common characteristics include regular periodicity, potential variation in amplitude, and dependence on calendar effects. They are typically isolated using decomposition methods or modeled directly with components that capture seasonality.

Modeling approaches include seasonal decomposition of time series (classical additive or multiplicative), STL (seasonal-trend decomposition using Loess), and structural time series models like ARIMA with seasonal terms or state-space formulations. Fourier terms or seasonal dummy variables are used to encode periodic effects. Modern forecasting tools such as Prophet and X-13-ARIMA-SEATS incorporate flexible seasonality handling as part of broader models.

Applications span economics, retail, energy, tourism, and climate studies, where understanding timesseasonal helps improve forecast accuracy and interpretation of seasonal effects.

Related concepts include seasonality, trend, and irregular components, as well as measures of seasonal strength and seasonality indices used in decomposition outputs.