# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 5 Results and Analysis
### Analysis of Results
The statistical analysis of the results in Figure 6 reveals that the mean value of the absolute error when using only the original features was 0.5177 \({}^{\circ}\)C, and the standard deviation of the absolute error was 0.5652 \({}^{\circ}\)C. The mean value of the absolute error when also using the time-series features was 0.4491 \({}^{\circ}\)C, and the standard deviation of the absolute error was 0.5259 \({}^{\circ}\)C. The mean value and standard deviation of the absolute error when also using the time-series features were less than those when using only the original features, and the prediction result obtained when also using the time-series features was closer to the real value than that obtained using only the original features, and the fluctuation of the error was smaller. Figures 7 and 8 show that when the temperature trend changed (i.e., from decreasing to increasing or from increasing to decreasing), the prediction results of the BP neural network had lower absolute errors after time-series features were added. Statistical analysis of the  
Figure 8:0 Absolute error of the MLP model with and without the time-series features. Figure 8:1 Fitting trend of the prediction results of the MLP model. results in Figure 8 revealed that the mean value and standard deviation of the absolute error were 0.5852 and 0.6146 \({}^{\circ}\)C, when using only the original features, and 0.5617 and 0.5640 \({}^{\circ}\)C when also using the time-series features. Therefore, the prediction result of the BP neural network when also using the time-series features was better than that when using only the original features, but worse than that of LightGBM. The prediction results and absolute errors of the SVM model are shown in Figures 9 and 10. Statistical analysis of the results in Figure 10 showed that the mean value and standard deviation of the absolute error of the SVM model were 0.5111 and 0.5374 \({}^{\circ}\)C, when using only the original features, and 0.4917 and 0.5476 \({}^{\circ}\)C when also using the time-series features. Therefore, the prediction result of the SVM when also using time-series features was better than that when using only the original features, but the error fluctuation was larger, and it was better than that of the BP neural network with time-series features but worse than that of LightGBM. The prediction results and absolute errors of linear regression model are shown in Figures 11 and 12. Statistical analysis of the results in Figure 12 showed that the mean value of the absolute error of linear regression when using only the original features was 0.4988 \({}^{\circ}\)C, and the standard deviation of the absolute error was 0.5414 \({}^{\circ}\)C. The mean value of the absolute error when also using the time-series features was 0.5030 \({}^{\circ}\)C, and the standard deviation of the absolute error was 0.5732 \({}^{\circ}\)C. In contrast with the prediction results of the previous model, the mean value and standard deviation of the absolute error in the prediction results of the linear regression model increased after adding the time-series features. The ability of linear regression to distinguish the time-series features is thus not strong, which relates to the characteristics of the linear regression model itself. The degree of fitting of the prediction curve and the real curve in Figure 13 and the fluctuation of the absolute error in Figure 14 reveal that the absolute error in the prediction result of the time-series features of the RBF model was lower than that of other models approximately 52 h previous, but the error in the subsequent results was similar to that of the prediction result using the original features and worse than that of other models. The RBF model has better recognition ability for short-term time-series features but poor recognition ability for long-term time-series features.