# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 5 Results and Analysis
### Analysis of Results
This characteristic can weaken the effect of abnormal data, and the recognition of the complex features constructed in this paper is the same as that of the original features, which are divided into branches depending on the optimal threshold value. The MSE, MAE, and R2_score of the sub-optimal SVM model decreased by 1.55% and 3.80% and increased by 0.03% after adding the time-series features. The SVM model can specify different kernel functions in the decision function, and the transformation can be nonlinear (i.e., the transformation space can be high-dimensional), such that its input space can be nonlinear, which is of great help in the recognition of high-dimensional complex features. Therefore, the SVM can still achieve excellent performance after adding time-series features. Both BP and RBF neural network models have the ability of multi-dimensional nonlinear mapping, self-learning, and self-adaptation, such that they can also identify high-dimensional complex features. Since each model has a different base learner and recognition ability for high-dimensional complex features, the time-series information that each model can recognize is also different to some extent. The prediction effect can thus be improved by adding time-series features. Owing to the shortcomings of the linear regression model, it is difficult to model nonlinear data or features with correlation and to identify highly complex features well. Therefore, the difference features, statistical features, and combination features mentioned in this paper do not perform well in the linear regression model. Compared with linear regression, the MLP model can fit nonlinear data and deal with complex relationships more \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **MSE\({}_{1}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MAE\({}_{2}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **R2\({}_{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\}\) \\ \hline \multirow{2}{*}{\begin{tabular}{c} **LightGBM** \\ BP \\ SVM \\ Linear Regression \\ RBF \\ MLP \\ \end{tabular} } & 0.7193 & 0.5852 & 0.9706 & 0.6329 & 0.5617 & 0.9742 & 12.01\% & 24.54\% \\  & 0.5494 & 0.5111 & 0.9776 & 0.5409 & 0.4917 & 0.9779 & 1.55\% & **11.70\%** \\ \multirow{2}{*}{\
\begin{tabular}{c} Linear Regression \\ RBF \\ MLP \\ \end{tabular} } & 0.5413 & 0.4988 & 0.9779 & 0.5808 & 0.5030 & 0.9763 & -7.30\% & 17.77\% \\  & 0.7254 & 0.5901 & 0.9704 & 0.6739 & 0.5749 & 0.9275 & 7.10\% & **29.13\%** \\ \multirow{2}{*}{
\begin{tabular}{c} Linear Regression \\ RBF \\ MLP \\ \end{tabular} } & 0.7254 & 0.5904 & 0.5753 & 0.5141 & 0.9765 & 9.54\% & 16.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation indicators of the models before and after adding time-series features. flexibly. Therefore, the MLP model can better identify the complex features constructed in this paper, and the prediction indicators of the MLP model have been greatly improved after the addition of time-series features. Compared with the predication without time-series features, the MSE decreased by 9.54%, the MAE decreased by 1.68%, and the R2_score increased by 0.26%.