# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 1 Introduction  
Temperature prediction plays a crucial role in modern society, with applications spanning across multiple fields [1]. Firstly, temperature prediction can assist meteorological departments in issuing early warnings for weather-related disasters, thereby reducing casualties and property losses. Secondly, the temperature is a vital factor in the growth and development of crops, so accurate temperature prediction helps to increase crop yields and ensure food security. Additionally, temperature prediction is of significant importance in transportation, energy production and supply [2], urban planning [3], health management [4], and disease control, as well as environmental protection [5] and ecological balance. Therefore, continuously improving the accuracy and reliability of temperature prediction is essential for addressing climate change [6], ensuring public safety, and promoting sustainable development. Temperature prediction serves domain-specific critical functions: agriculture relies on growing-degree days (GDD) forecasts to optimize crop cycles; energy systems use hourlyload predictions to prevent grid failures; public health organizations employ heatwave alerts to reduce mortality; transportation requires road-surface temperature forecasts for accident prevention; and climate policy depends on decadal projections for emission strategies. Each application demands distinct prediction horizons and accuracy thresholds, collectively justifying the need for modeling that is robust against inherent non-stationarity and noise. Temperature prediction has a significant impact on agricultural production [7] and food security. Temperature is one of the key factors that affects crop growth and development, and its accurate prediction helps farmers in making informed decisions regarding the planting time, fertilization, and irrigation of their crops, as well as other agricultural activities. This, in turn, leads to increased crop yield and quality, ensuring food supply and agricultural product safety [8]. Temperature prediction also holds significant importance in energy production and supply, transportation, urban planning, health management, and disease control, as well as environmental protection and ecological balance. In terms of energy production and supply, accurate temperature prediction assists the power and gas industries in effectively planning their energy supply, improving energy utilization efficiency and reducing energy costs. Concerning transportation and urban planning, temperature prediction helps transportation management departments in implementing effective traffic control measures, reducing traffic congestion and accidents, and enhancing the efficiency of urban transportation. Regarding health management and disease control, the temperature directly impacts human health, so accurate temperature prediction aids health departments and medical institutions in taking timely measures to prevent and address temperature-related health issues. In certain specific fields, such as greenhouse temperature control (e.g., MPC and RL optimizations [9], GRU-based multi-scale predictions [10]), photovoltaic panel temperature modeling (hybrid numerical-ML methods [11]), and soil temperature estimation (spatiotemporal deep learning frameworks [12]), effective temperature management and prediction are required. In the realm of environmental protection and ecological balance, temperature prediction contributes to monitoring and assessing the quality of the environmental, preventing and reducing environmental pollution, protecting ecosystems and biodiversity, and promoting sustainable development and ecological balance. The current methods for temperature prediction include traditional physical model approaches and data-driven machine learning methods. Here are some common temperature prediction methods. First, the traditional numerical [13] weather prediction models entail a forecasting method based on atmospheric physics equations and numerical calculation techniques.