Glossary

Seasonality

A regular, predictable pattern in data that repeats at a fixed calendar interval, such as daily, weekly, or annually.

Seasonality is a pattern in a time series that recurs at a fixed, known interval: retail sales spiking every December, website traffic dipping every weekend, or call center volume peaking every morning. Unlike a general trend, which moves gradually in one direction, seasonality is cyclical and repeats at the same points in each cycle.

Analysts identify and remove seasonality through seasonal decomposition, which splits a series into trend, seasonal, and residual components, so the underlying direction of a metric can be judged without the recurring cycle masking it. A related but distinct concept is a business cycle or multi-year cycle, which repeats over a longer, less fixed period and is not usually called seasonality.

Seasonality matters because comparing raw values across a seasonal cycle without adjusting for it produces misleading conclusions, comparing December sales to November sales looks like explosive growth even in a flat year, simply because of the holiday season. time series forecasting and demand forecasting models explicitly account for seasonal components to avoid this. The common pitfall is mistaking a seasonal spike for a genuine outlier or a real shift in behavior, or conversely, assuming a pattern is seasonal without enough historical cycles to confirm it repeats reliably.

Last reviewed September 22, 2026

In the index now

Related terms

Moving average A calculated series that averages a fixed window of recent data points, smoothing out short-term noise to reveal a trend. Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Demand forecasting Estimating future customer demand for a product so inventory, production and staffing can be planned ahead. Outlier An observation that differs markedly from the rest of the dataset, either from genuine variation or from an error.

Related guides

How to choose a forecasting platform or library Pick a time-series forecasting library by how many series you're forecasting and how much modeling control your team wants. How to choose a weather data API Pick a weather provider by how it sources its forecasts, what resolution you need, and whether you want raw data or a decision layer on top. How to forecast demand Baselines worth beating, where seasonality and model choice actually matter, and how to judge accuracy honestly. How to run marketing mix modeling What data an MMM needs, the build-vs-buy choice between open-source libraries and managed vendors, and how to validate the model with real experiments. Time series analysis, explained How analysts model data ordered in time — the patterns forecasting methods look for, the pitfalls, and where the data actually lives.