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Probabilistic Seasonality

Feras A. Saad, Todd B. Walker

arXiv 28 Sep 2026 · Econometrics

arXiv:2609.36280 · PDF · Extracted main text

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.

Citation extraction

35
references
52
in-text mentions
35
distinct cited
4
self-citations
15,491
main-text words

appendix boundary found by appendix_command · 82% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1U.S. Census Bureau (2025) X-13ARIMA-SEATS Reference Manual1.00053100%
2Saad, Feras A. and Patton, Brian J. and Hoffmann, Matthew D. and Sau… (2023) Sequential Monte Carlo Learning for Time Series Structure Discovery self0.87452100%
3Rasmussen, Carl E. and Williams, Christopher K. I (2006) Gaussian Processes for Machine Learning0.7374350%
4Box, George E. P. and Jenkins, Gwilym M (1976) Time Series Analysis: Forecasting and Control0.73732100%
5Clive W. J. Granger (1978) Seasonality: Causation, Interpretation, and Implications0.64422100%
6MacKay, David J. C (1998) Introduction to Gaussian Processes0.5112250%
7Brown, Robert G (1963) Smoothing, Forecasting and Prediction of Discrete Time Series0.51121100%
8Findley, David F. and Lytras, Demetra P. and McElroy, Tucker S Detecting Seasonality in Seasonally Adjusted Monthly Time Series0.51121100%
9U.S. Bureau of Labor Statistics The Employment Situation–-July 20250.40511100%
10U.S. Bureau of Labor Statistics (2026) Seasonal Adjustment Methodology for National Labor Force Statistics0.40511100%

Showing the top 10 of 35 scored citations.