# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 5 Experimental Procedures
### Evaluation of Experimental Outcomes Utilizing Genuine Data  
In our investigation to assess the applicability and efficacy of EEMD-LR, we opt for actual meteorological temperature data for predictive analysis. Temperature, being a component of meteorology, exhibits substantial fluctuations that are largely independent of human influence. Its predictability holds significance for exploring EEMD-LR's utility in meteorological applications.  
The results obtained from the NuSVR experiments outlined in Table 3 demonstrate that it does not perform better than the other methods in predicting data for temperature and sine waves. The consistency and reliability of the temperature and sine series fluctuations suggest that NuSVR is more suitable for predicting such consistent patterns. Despite being a form of support vector regression, NuSVR has been enhanced with improvements that allow for greater flexibility in penalty and loss function selection, which is particularly beneficial for larger sample sizes. Notably, the experimental results in Table 3 indicate that NuSVR's predictive accuracy does not rank the highest among the tested time-series data.  
Table 3 reveals that LSTM struggles in forecasting temperature time-series data but shows marginal improvement with sine time-series data. This is unexpected given the advancements in deep learning algorithms, and suggests that LSTM should excel. However, the experiment's results imply that the LSTM model construction, parameter configurations, and training iterations may have influenced its underperformance.  
In short, our proposed EEMD-LR model achieved RMSE, MAE, and R\({}^{2}\) values of 0.000027, 0.000021, and 1.000000, respectively, on the sine simulation time-series data. For the actual temperature time-series data, the model achieved RMSE, MAE, and R\({}^{2}\) values of 0.713150, 0.512700, and 0.994749, respectively. The experimental results on these two datasets indicate that the EEMD-LR model demonstrates superior predictive performance compared to alternative methods.  
Table 3 presents a comparison of the RMSE evaluation metrics for the predictions of LSTM and EEMD-LSTM on the temperature time series. Notably, the EEMD-LSTM method exhibits superior performance over the standard LSTM approach across both temperature and sine time series. Figure 17 visually depicts the EEMD-LSTM prediction results for the temperature.  
Figure 17: The EEMD-LSTM method’s prediction results for temperature.  
The forecasting results of LR and EEMD-LR for the temperature were evaluated using the MAE and RMSE evaluation indexes, as observed in Table 3. The comparison of the evaluation indexes reveals that EEMD-LR outperforms LR in terms of its RMSE and MAE. Moreover, Figure 18 illustrates the temperature forecasting results achieved by EEMD-LR.  
The results from Table 3 indicate that hybrid methods, such as EEMD-LSTM and EEMD-LR, outperform the initial LSTM and LR approaches across temperature and sine wave datasets. Notably, the EEMD-LR and LR methods demonstrate promising experimental outcomes. This success can likely be attributed to the EEMD algorithm being employed in the hybrid methods, which decomposes the complex sequences into stable and regular sub-sequences, enhancing the models' predictability and efficacy. To elucidate the EEMD decomposition and wind speed variations, Figure 19 illustrates the sequence and sub-components changes post-EEMD decomposition. The initial sub-graph in Figure 19 depicts the initial sequence shift, while the subsequent sub-graphs are EEMD decomposition sub-graphs, showcasing increased stability, regularity, and simplicity.  
Figure 19: The temperature time series undergoes EEMD decomposition to produce its decomposed components.  
Figure 18: The EEMD-LR method’s prediction results for temperature.