# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 1 Introduction
These models divide the atmosphere into three-dimensional grids and simulate atmospheric dynamics and thermodynamics processes to predict temperature changes over a future period of time. Some of the well-known prediction models for weather include the Medium-Range Weather Forecasts of European Centre, the Global Forecast System (GFS), and Environmental Prediction, which was developed by the National Centers, among others. These models provide high-resolution weather forecasts and are suitable for short-term and medium-term predictions. Second, statistical methods [14] represent an approach that uses historical observation data and statistical models to forecast future temperature changes. These methods include time-series analysis, regression analysis, and probabilistic statistical methods, among others. Statistical methods are commonly used for short-term and medium-term weather forecasts, particularly when complex physical process simulations are not available. They can provide simple and effective predictions. Third, machine learning methods [15; 16] have gained attention in recent years as a temperature prediction approach. These methods utilize a large amount of observation data and model input data to train machine learning models to forecast future temperature changes. Typical machine learning techniques encompass support vector machines, decision trees, deep learning methods, neural networks, and various other approaches. These methods are suitable for long-term and global-scale climate simulations and can handle complex nonlinear relationships and multivariate data, achieving certain prediction results. Fourth, the ensemble forecast method [17] is an approach that enhances the accuracy of temperature prediction by constructing a collection of models with different initial conditions or parameter settings, and then conducting statistical analysis on these model ensembles. This method effectively reduces the predictive errors of individual models and thereby improves the reliability and confidence of forecasts. It is commonly used for long-term and global-scale predictions [18]. In fact, methods can be diverse and mixed methods, such as Bayesian models for probabilistic temperature prediction, physical information neural networks for heat transfer modeling, and \"perfect model\" framework research for regional climate prediction [19]. Fifth, time-series analysis [20; 21; 22; 23] is a statistical method used to forecast temperature changes. It identifies temperature change patterns by examining the periodicity, trend, and seasonality in historical data. Secondly, it utilizes models such as ARMA, ARIMA, etc., to fit the historical data and predict future temperatures. It has an improved prediction accuracy due to incorporating external factors such as meteorological data. Although it cannot capture complex processes, it has certain applications in short-term and medium-term forecasts. Some studies have used mixed time-series methods, and here are some references to groundbreaking work--the CEEMDAN-PSO hybrid model [24] for price prediction--and recent developments--the HHO-LSTM-SVR [25] for TBM parameter prediction and the ARIMA-ANN hybrid model [26] for high-frequency financial data. Sixth, data assimilation techniques [27; 28] involve combining observational data with numerical model simulations and using optimization methods to adjust the model state, which enhances the temperature prediction accuracy of the model. Data assimilation techniques can effectively utilize multiple sources of observational data, correct model errors and uncertainties, and improve the spatial and temporal resolution of temperature predictions. It is an important means of improving the accuracy of temperature prediction. Finally, transformer-based models [29] have demonstrated significant advantages in time-series forecasting.