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

presented in Fig 4c for daily temperature prediction, three input lags (2,3, and 4 days) are con- sidered for the model development. As shown in Table 7, the ARIMA model performs on par with the QRT model and underperforms in comparison to the other models for daily scale. Furthermore, the ARIMA model requires only three input lags (ARIMA 3,1,2) to reach its best performance. On top of that, the application of the data transformation approach enhances the efficiency of the ARIMA prediction considerably. On the other hand, compared to the ARIMA model, the RF improves the prediction accuracy by 3% in terms of R and reduces the prediction error by 52.33%, 51.16%, and 52.1% in terms of RMSE, U, and MAE, respectively. For weekly temperature prediction, as presented in Table 8, the RF model demonstrates excellent performance in weekly temperature prediction by providing high prediction accu- racy with R ranging from 0.933 to 0.982 and fewer prediction errors with RMSE ranging from 2.478 to 4.665, U ranging from 0.091 to 0.173 and MAE ranging from 1.874 to 3.614 compared to the other models. Furthermore, the high performance of the RF model was achieved using only one input lag (RF − M10), while the other models require significantly higher input lags (seven lags) to reach their optimum performance (SVR − M16, RT − M16, GBR − M16, Fig 4. Input determination for ARIMA model, a) is the original data, b) after applying differencing method, c) PACF for daily scale. https://doi.org/10.1371/journal.pone.0277079.g004 PLOS ONE Air temperature forecasting: Mutli-time scale

PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 16 / 31