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The Advanced Retail Sales report was released this morning. Retail sales data contain a strong seasonal component, which can make the short-term signal difficult to separate from the recurring calendar pattern.
For fun, I created an epicycle visualization in RainbowStats to show how a time series can be reconstructed using a collection of rotating circles. Each circle represents a harmonic component. When the components are added together, the path recreates the original retail sales series.
Main idea: what appears to be a complex economic time series can often be represented as the sum of many simple cycles.
Epicycles provide a visual way to understand Fourier decomposition. Instead of looking only at coefficients or frequencies, the animation shows the mechanics directly: circles rotate, their vectors add together, and the final point traces the reconstructed data path.
This is especially useful for highly seasonal data. Retail sales rise and fall with holidays, weather, school calendars, and consumer behavior. The epicycle animation makes that repeating structure visible.
The movie reconstructs the retail sales series by combining harmonic components. Large seasonal patterns appear first, while smaller circles refine the shape and help capture more detailed movement in the data.
The result is part economics, part signal processing, and part visual explanation.
epicycles(logdiff(RSAFSNA),12)
RainbowStats continues to evolve as a platform for combining financial modeling, econometrics, and interactive visualization.
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