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Dictionary / Seasonality

Seasonality

Last updated August 14, 2023

What is seasonality?

Seasonality refers to the characteristic patterns or variations that occur in a particular phenomenon, process, or data set over distinct time intervals or seasons. It describes the regular, predictable fluctuations that repeat within a given time frame, such as days, weeks, months, or years.

Seasonality is commonly observed in various fields, including economics, finance, weather patterns, and many others.

What factors influence the occurrence of seasonality?

In seasonal patterns, certain factors influence the occurrence of predictable patterns, such as weather conditions, holidays, or natural cycles. These factors can result in recurring patterns of behavior or demand, leading to fluctuations in data or activity over time.

For example, retail and fashion sales often experience spikes during holiday seasons. Christmas, New Year, Black Friday, and other festivities can lead to increased consumer spending, travel, and specific purchasing patterns.

Seasonal collections all depend on accurate forecasting. That’s why helps brands align assortments and channel timing with seasonal patterns, while ensures those fluctuations are reflected in SKU-level financial targets.

FAQ

Seasonality involves repeating patterns within shorter time intervals, while trends show long-term shifts in data over extended periods. Seasonality is predictable and short-term, while trends might evolve gradually over years.

  • Additive seasonality: A constant amount of change is added to the data points across all periods.
  • Multiplicative seasonality: The change in data values is proportional to the level of the data.

Seasonality can be detected by visualizing data using line plots, scatter plots, or seasonal sub-series plots. Additionally, statistical tests and decomposition methods can help identify seasonal patterns.

Seasonality can be removed through methods like seasonal decomposition, where the data is separated into trend, seasonal, and residual components. This allows for clearer analysis of underlying trends.