Seasonality refers to the predictable and recurring fluctuations in data that happen over a specific period. Typically, these fluctuations are related to changes in weather, holidays, or cultural events. In business, seasonality affects sales patterns, staffing needs, and consumer behavior. For example, retail sales often peak during the holiday season. Recognizing these patterns is essential because they can mask underlying trends in the data. Understanding seasonality helps businesses better prepare for these fluctuations and adjust their strategies accordingly.
Seasonality and Macroeconomic Indicators
Seasonality plays a significant role in interpreting macroeconomic indicators like GDP. Many economic statistics are reported both as raw (unadjusted) and seasonally adjusted data to offer a clearer picture of underlying trends, free from predictable seasonal influences. For example, about 25% of U.S. GDP fluctuations can be attributed to seasonal changes such as holiday spending or end-of-year tax measures. Seasonal adjustments also capture subtler patterns, like increased consumer spending on paydays. One common method for isolating these seasonal effects is the X-13 procedure[1], developed by the U.S. Census Bureau, which breaks down data into seasonal, trend-cycle, and irregular components, allowing analysts to focus on long-term trends without distortion from regular seasonal fluctuations.
The Importance of Seasonally Adjusting Data
When predicting a time series, it is crucial to ensure that the historical data of the variables being predicted is seasonally adjusted. Failing to account for seasonality can result in misleading conclusions, as the model may interpret regular seasonal fluctuations, like holiday sales spikes or back-to-school shopping surges, as lasting trends. By adjusting for these predictable patterns, you allow the model to focus on the true underlying factors driving sales. This leads to more accurate and reliable predictions, especially when analyzing historical data to forecast future performance.
Ready Signal
We’ve enjoyed helping our clients understand the seasonality of their data to uncover deeper knowledge about what is driving traffic and interest in the businesses they serve.
Let us help you! Ready Signal is an external data platform that helps users discover, test, and integrate data from various external sources to build better predictive models. By properly accounting for seasonality, models can better align with real-world dynamics, enabling more precise forecasts and improving strategic decisions in response to market changes.
If you’re curious about seasonality and how data from external sources can help you understand what’s happening inside your business, get in touch today!
Keep Learning About Seasonality
If you’d like to keep learning about seasonality, here are some additional resources that might be of interest:
Seasonality (kaggle.com)
Why economists care about “seasonally adjusted” data – Marketplace
Time Series and Seasonal Adjustment (census.gov)
[1] https://www.census.gov/data/software/x13as/about.html
