Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is estimated using a kernel function. The smoothing parameter is obtained by a cross-validation procedure and used for the selection of the optimal number of closest curves. The other method applied is a Seasonal Autoregressive Moving Average (SARMA) model, the order of which is determined by the Bayesian Information Criterion. The obtained forecasts are combined using weights calculated based on the forecast errors. The results show that SARMA has a better performance for the first 6 forecasted hours, after which the Non-Parametric Functional Data Analysis (NPFDA) model provides superior results. Forecast pooling improves the accuracy of the forecasts.

气温是影响社会活动和经济部门的重要气象变量。在本文中，使用非参数和参数方法来提前24小时预测每小时的气温。前者是功能数据分析框架中的回归模型。使用核函数估计非线性回归算子。平滑参数是通过交叉验证过程获得的，并用于选择最佳数量的最接近曲线。应用的另一种方法是季节性自回归移动平均值（SARMA）模型，其顺序由贝叶斯信息准则确定。使用基于预测误差计算的权重合并获得的预测。结果表明，SARMA在预测的前6个小时中表现更好，之后，非参数功能数据分析（NPFDA）模型可提供出色的结果。预测池可提高预测的准确性。