# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 6 Discussion  
In this paper, we extracted the time-series features of the factors affecting the indoor temperature in a greenhouse and then used the LightGBM model to more accurately predict the greenhouse temperature. It was found in the experiment that linear regression did not have a strong ability to identify high-dimensional complex features, and therefore its MSE and MAE for prediction did not decrease with the inclusion of time-series features. Indeed, the original feature prediction performance was better. The combination of the LightGBM model and time-series features greatly improved the accuracy of prediction results. Although models other than the linear regression model have improved the prediction performance after adding time-series features, the positive effect on the performance of the LightGBM model was more obvious. In terms of the required training time, following cross-validation, the training time required for most models is directly proportional to the number of cross-validation folds, and the proportionality coefficient of the LightGBM model was relatively moderate. Although the LightGBM model was not the fastest, it obtained the optimal results in a relatively short time.  
Owing to the limitation that the training data provided by the competition platform are only for March and April, the method proposed in this paper has not been verified using data for other periods, and we have checked relevant works published in the past two years and have not found any other study using this dataset. Therefore, there is no comparison between presented results and the results from the existing literature that deals with similar research. Moreover, the limited original feature information in the training data limited the time-series feature information that we could extract, such that some factors affecting the greenhouse temperature were not considered. If other quarterly data are added and the volume of data is sufficient, because the LightGBM model is based on the GBDT algorithm, the temperature corresponding to different quarters will be divided into different branches when building the boosting tree. In addition, if more influencing factors such as the light intensity and carbon dioxide concentration are added, more information can be extracted during time-series feature construction. Therefore, if the data of different quarters are taken as input, theoretically, the model can also learn the time-series information of different quarters.  
Time-series cross-validation is highly dependent on the ability of the base learner of the model to recognize historical time-series information. Stronger recognition ability means that more information will be extracted with an increase in the number of time-series features; otherwise, less information will be extracted. Therefore, when the data used for cross-validation are divided into multiple folds, a greater number of folds results in each fold containing less historical information. In the case of a multi-fold partition, the model can extract different historical time-series information from the data of each fold, which leads to a difference in the prediction results. A slight difference can help the model adjust parameters and improve the model performance, while large differences will make the model unstable and worsen the prediction results.