Sezónnost

Sezónnost, or seasonality, refers to regular and predictable fluctuations in time series data that repeat within a fixed period, typically within a year. It arises from recurrent calendar and climate factors such as weather, holidays, school and work cycles, and agricultural schedules. Seasonality can be additive, where fluctuations stay roughly the same in size, or multiplicative, where fluctuations grow with the level of the series.

Seasonal patterns often vary across domains. Retail sales may rise in holiday months, tourism can peak in summer, and energy demand may follow daily or seasonal temperature changes. Calendar effects, such as month length or major holidays, also contribute to observed seasonality. Distinguishing seasonal components from long-term trends, cycles, and irregular noise is essential for accurate interpretation and forecasting.

Measurement and analysis of sezónnost typically involve decomposing a time series into components: trend, seasonal, and irregular. Seasonal indices or factors summarize the average seasonal effect for each period (for example, each month or quarter). Seasonal adjustment methods, including classical decomposition and more advanced approaches like STL or model-based procedures, remove or isolate seasonality to facilitate comparison and modeling. Forecasting models often incorporate seasonal terms (for example, seasonal ARIMA) to capture the recurring patterns.

Applications span finance, retail, tourism, energy, and public policy. Understanding sezónnost supports planning, budgeting, inventory management, and scenario analysis. Limitations include potential changes in seasonal behavior over time due to climate change, policy shifts, promotions, or structural breaks, requiring ongoing monitoring and re-estimation.