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<p>This study analyzes historical temperature trends using Generalized Autoregressive Score (GAS) models, which capture time-varying dynamics in time series data. Using temperature data from Czechia, we examine average temperatures as well as extremes, including maximum and minimum values, and identify a consistent upward trend across all indicators, particularly since 1950. The GAS models demonstrate superior in-sample fit and out-of-sample forecasting performance compared to traditional approaches such as ARMA and GARCH models. Forecasts based on the GAS model project a substantial increase in temperatures of 1.29 °C over the next 40 years.</p>

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Score-driven models for air temperature analysis

  • 期刊论文

  • 发表日期：

    2026年4月12日

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  • Alžběta Šumníková,
  • Petra Tomanová

摘要

This study analyzes historical temperature trends using Generalized Autoregressive Score (GAS) models, which capture time-varying dynamics in time series data. Using temperature data from Czechia, we examine average temperatures as well as extremes, including maximum and minimum values, and identify a consistent upward trend across all indicators, particularly since 1950. The GAS models demonstrate superior in-sample fit and out-of-sample forecasting performance compared to traditional approaches such as ARMA and GARCH models. Forecasts based on the GAS model project a substantial increase in temperatures of 1.29 °C over the next 40 years.