## 1 Introduction  
Forests play an indispensable role in global ecosystems, offering vital services such as oxygen production, air purification, soil and water conservation, climate regulation, carbon cycling, and the preservation of biodiversity, in addition to providing habitats for numerous species [1, 2]. In recent years, the prevalence of extreme fire weather has increased worldwide due to global warming and prolonged fire seasons, leading to an expansion in both the extent and average duration of forest fires across many regions, thereby altering the structure and diversity of forest ecosystems. These fires also pose serious threats to human life, regional economies, and environmental health [3, 4, 5, 6, 7, 8]. Between 2001 and 2021, forest cover loss due to wildfires reached 1.19 million hectares globally, with China experiencing a loss of 893,000 hectares within this twenty-year period. From 2003 to 2018, China witnessed 111,446 forest fires, affecting an area of 3.2895 million hectares and an annual average burned area of 205,600 hectares [9]. These figures underscore the global concern for forest fire prediction and prevention. Therefore, understanding the factors influencing forest fire occurrences is crucial for fire prevention, suppression, forest conservation, and resource allocation. Additionally, the ability to predict the number of fires and the extent of burned areas is essential to avert many accidents and mitigate the destructive consequences of fires.  
To achieve this goal, we conducted predictive analyses of forest fires. Forest fire prediction is not merely a short-term forecasting challenge but a complex task involving comprehensive analysis across multiple disciplines, scales, and technologies. As Figure 1 illustrates, fire occurrence is influenced by various factors. Natural factors include weather conditions, climate change, and fuel characteristics [10; 11; 12]. Rising temperatures facilitate easier ignition of fuel, leading to more intense fires. In conditions of low humidity, fuels dry out more easily, increasing fire likelihood. Strong winds can rapidly spread fires and alter their paths. Prolonged droughs and reduced rainfall heighten fire risks, while sufficient rainfall reduces them. These climatic changes also alter regional precipitation patterns, profoundly impacting fire risks.  
Human factors encompass human activities, fire management, and land-use changes [13; 14; 15; 16]. Human activities, such as accidental fires, campfires, and fireworks, are major causes of many wildfires. Implementing fire management strategies can help mitigate the impact of fires. Furthermore, deforestation, agriculture, and urbanization alter fuel distribution and types, thereby affecting fire risks. Land-use changes can lead to shifts in plant communities, affecting the combustibility of materials. Additionally, the layout of urban road networks and buildings also influences the spread of fires.  
This paper collects forest fire and meteorological data from Yunnan Province, constructs stepwise regression and ARIMA models, and analyzes factors like fire frequency, fire area, average temperature, annual rainfall, humidity, and wind speed from 1991 to 2021. Through time series and multivariate analyses, we comprehensively understand forest fire dynamics, which is crucial for fire prediction and fire management. This study innovates  
Figure 1: Map of fire influencing factors.  
in the field of forest fire prediction, combining traditional stepwise regression analysis with modern time series analysis techniques, not only enhancing prediction accuracy but also providing new perspectives and tools for fire strategies and forest management.