Data-driven models for atmospheric air temperature forecasting at a continental climate region

examined with different times of lags. The daily scale showed that the RF technique provided more accurate outcomes than the comparable models. Moreover, the advanced analysis of forecasting error exhibited that the performance of the models was significantly affected by data variability, consistency, and extreme temperature val- ues. As January, February and December had higher variability of temperature data values, the effect on the model performance was greater for these months. In addition to this, the forecast- ing errors observed for these months were higher than other months due to the fact that the average temperature observed for these months fell below the overall average temperatures observed for the entire dataset. The models performed very well for the weekly time scale, but the RF, modeling technique provided more accurate results compared to other models. In general, the accuracy of daily Fig 11. The histogram and Gaussian kernel density function for weekly temperature prediction during the testing phase. https://doi.org/10.1371/journal.pone.0277079.g011 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 24 / 31