# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 2 Related Work  
Recent theoretical research on the temperature of a greenhouse environment mainly includes the investigation of the prediction model of energy and the material balance equation, but the thinking of factors affecting the indoor environment is too singular, and time-series features and environmental factors of a greenhouse have not been comprehensively considered [1; 2]. [PERSON] et al. [3] adopted the improved support vectormachine (SVM) and grid search algorithm. It is possible to dynamically optimize the kernel parameters and penalty factors of the SVM, but the grid search efficiency is too low, and more computing resources are needed to handle increasing data volumes. [PERSON] et al. [4; 5] used the least-squares method to predict the temperature curve but only predicted the future temperature according to the historical change in temperature and did not consider other environmental factors, such as the air pressure and humidity. [PERSON] et al. [6; 7] established a model for predicting the greenhouse indoor temperature adopting a radial basis function (RBF) neural network, which has a high training speed and does not fall into local optima, but they did not consider the nonlinear effects of environmental factors on the greenhouse temperature. [PERSON] et al. [8; 9] adopted particle swarm optimization (PSO) to optimize the prediction of the greenhouse temperature. A PSO algorithm can optimize the RBF neural network structure and network weights can improve the training performance. However, with increases in the number of sample data, the number of model iterations, and particle size, the prediction results of the model are prone to be trapped in local optima. [PERSON] et al. [10; 11] tested and predicted the influencing factors of the indoor temperature adopting a dynamic model and multiple linear regression. They comprehensively considered a variety of environmental factors, such as the indoor temperature and humidity and outdoor temperature. However, some factors of multiple linear regression are not measurable, and it must be assumed that the environment is a small ecosystem in which the conservation of energy holds, but this situation does not occur in reality. [PERSON] et al. established a temperature prediction model for the greenhouse ecosystem based on the back-propagation (BP) neural network [12; 13]. The model has the characteristics of a simple structure, low calculation cost, and strong adaptability. However, there are many minima in the BP neural network, such that it is easy to fall into local minima. A large number of training times leads to a low learning efficiency and slow convergence. The selection of the hidden layer lacks theoretical guidance, and there is a tendency to forget the old samples when training and learning new samples. [PERSON] et al. carried out the time-series analysis of a hydrological climate and extracted many time-series features, such as trend features and extreme features, which affected the accuracy of the hydrological climate prediction [14]. In recent years, multivariate time series was proposed. [PERSON] et al. [15] proposed a theory of state space reconstruction of multivariate time series based on the non-uniform embedding method of information theory, and in [16] they present a new non-uniform embedding method framed in information theory to detect causality for multivariate time series, named LM-PMIME. The key to solving the problem of multivariate time-series prediction lies in the description of the relationship between the variables. Only by accurately and clearly expressing the mutual influence of each variable can we achieve good prediction results. In other words, the prediction effect of multivariate time series with an unclear relationship is not satisfactory. In view of the above problems, this paper uses the light gradient boosting machine (LightGBM) model as the basic prediction model.