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Probabilistic Seasonality

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  • Feras A. Saad
  • Todd B. Walker

Abstract

Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks.

Suggested Citation

  • Feras A. Saad & Todd B. Walker, 2026. "Probabilistic Seasonality," Papers 2609.36280, arXiv.org.
    • Handle: RePEc:arx:papers:2609.36280
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    File URL: https://arxiv.org/pdf/2609.36280
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    References listed on IDEAS

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    1. Victoria Consolvo & Kurt Graden Lunsford, 2019. "Residual Seasonality in GDP Growth Remains after Latest BEA Improvements," Economic Commentary, Federal Reserve Bank of Cleveland, issue April.
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