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

Furthermore, the ARIMA model requires only one input lag (ARIMA 1,1,1) for weekly predic- tion to reach its best performance. For further assessment, we compared the performance of the best models obtained from the daily prediction (SVR − M5, RT − M5, GBR − M6, QRT − M9, and RF-M1) for each month of the year. In other words, the daily temperature prediction may vary from month to month, so it is essential to investigate the performance of the applied models for each month. What supports the importance of conducting this investigation is the considerable variation in temperature during the months of the year (see Table 2). It can be observed that the Standard deviation (St.D) varies from 2.916 to 7.905. The other significant indicator is that the data length varies monthly (see Fig 6). Fig 7 shows the performance of the best models based on RMSE statistics for each month of the year. After the training process was completed, the performance of each model was assessed individually. In general, statistical metrics such as RMSE provide the model’s overall evaluation. Therefore, this figure is created to see the monthly performance of each model. It is observed that the models have faced problems in estimating the temperatures for the winter Fig 5. Input determination for ARIMA model., a) is the original data, b) after applying differencing method, c) PACF for weekly scale. https://doi.org/10.1371/journal.pone.0277079.g005 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 18 / 31