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<p>This study proposes a modeling framework based on piecewise constant regression with a Periodic Autoregressive (PAR) remainder. Model complexity is controlled using Rissanen’s Minimum Description Length (MDL) criterion, while Fourier-based compression reduces dimensionality without compromising likelihood, improving computational efficiency. A multi-island Genetic Algorithm (GA) jointly estimates changepoint (CP) configurations and seasonal order. Monte Carlo (MC) experiments under Dobrogea conditions show reliable detection of all three change points when shifts exceed 1.5 innovation standard deviations. Applied to Medgidia precipitation (1965–2019), the method identifies an early shift while retaining a parsimonious AR(1) structure. By separating true climatic shifts from noise, the framework provides a robust basis for water resource assessment and infrastructure planning.</p>

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Detecting Undocumented Shifts in Seasonal Precipitation: An MDL-Driven Periodic Autoregressive Segmenter with Fourier Compression and Genetic Search

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Youssef Saliba,
  • Alina Bărbulescu

摘要

This study proposes a modeling framework based on piecewise constant regression with a Periodic Autoregressive (PAR) remainder. Model complexity is controlled using Rissanen’s Minimum Description Length (MDL) criterion, while Fourier-based compression reduces dimensionality without compromising likelihood, improving computational efficiency. A multi-island Genetic Algorithm (GA) jointly estimates changepoint (CP) configurations and seasonal order. Monte Carlo (MC) experiments under Dobrogea conditions show reliable detection of all three change points when shifts exceed 1.5 innovation standard deviations. Applied to Medgidia precipitation (1965–2019), the method identifies an early shift while retaining a parsimonious AR(1) structure. By separating true climatic shifts from noise, the framework provides a robust basis for water resource assessment and infrastructure planning.