mevsimsellik

Mevsimsellik is the component of a time series that shows periodic fluctuations at regular intervals, such as monthly, quarterly, weekly, or daily. It reflects predictable patterns tied to seasons, holidays, daylight, and social behavior. In time series decomposition, data can be described by a trend component, a seasonal component, and an irregular component: y_t = T_t + S_t + I_t (additive) or y_t = T_t × S_t × I_t (multiplicative).

Estimation and adjustment of seasonality involve identifying recurring patterns and, if desired, removing them to study the underlying trend and irregularities. Common techniques include moving averages and seasonal indices, classical seasonal decomposition, and STL (seasonal-trend decomposition using loess). Modern methods and software implement seasonal adjustment tools such as X-13ARIMA-SEATS, which produce seasonally adjusted series and seasonal factors.

Applications of mevsimsellik span many fields, notably economics, finance, energy, tourism, and meteorology. Examples include higher retail sales during holiday periods, increased electricity demand in extreme seasons, or tourism peaks in specific seasons. Accurate modeling of seasonality improves forecasting accuracy and policy planning, as it helps distinguish regular seasonal effects from longer-term changes or shocks.

Mevsimsellik is distinct from cyclicality: seasonality is a regular, fixed-frequency pattern, while cycles are longer, less regular fluctuations not tied to a precise period. Challenges include evolving or changing seasonal patterns over time, calendar effects, and holidays, which may require flexible models or calendar-aware adjustments.