## 2 Literature Review  
The establishment of forest fire prediction models enables us to estimate the likelihood of forest fires and manage the areas at risk. Scholars have made substantial contributions to the field of fire prediction modeling. [PERSON] and colleagues have demonstrated the significance of using the ARIMA (Autoregressive Integrated Moving Average) model, combined with remote sensing technology and climate data, to analyze and predict the risk of forest fires in the Atlantic Forest, underscoring its importance for managing forest fire risks and its potential application in environmental protection and disaster prevention [17]. [PERSON] and others [18] have utilized the ARIMA model to forecast forest fire hotspots, emphasizing the importance of accurate predictions in fire prevention and emergency management and providing a practical case study for using time series analysis for prediction. [PERSON] and colleagues [19] have adjusted and optimized the parameters of the Seasonal ARIMA (SARIMA) model based on historical fire records, demonstrating how to more accurately predict the frequency of future fires and effectively identify high-risk periods, thereby contributing to improved disaster prevention and emergency preparedness. [PERSON] [20] used the ARIMA method to model the area burned by forest fires in Turkey, evaluating the model's effectiveness through Ljung-Box statistics, residual analysis, and cross-validation, forecasting the area affected by forest fires over the next three years.  
[PERSON] and others [21], through correlation analysis and regression models, found that the impact of most analyzed variables on forest fires varied within their study region during 2007-2017. [PERSON] and colleagues [22] demonstrated the effective application of GIS in fire management, damage assessment, and predicting future fires using stepwise and logistic regression to identify fire-related factors. [PERSON] et al. [23] used forest fire data and corresponding meteorological data from 2000-2013 in Dali State, employing logistic regression as a single model and stepwise regression to select the final variables for parameter fitting, finding that the Bayesian averaging model outperformed the binomial logistic regression model in terms of fit and predictive accuracy.  
In summary, while most research in forest fire prediction models has employed traditional analytical models, the ARIMA model boasts unique advantages in processing time series data and is particularly suited for analyzing and predicting events with strong temporal dependence. Time series analysis can reveal the cycles and trends of forest fire occurrences, which is crucial for understanding and predicting fires. However, the ARIMA model also has its limitations, requiring stationarity in the time series and being based on linear relationships, which may not fully capture the complex nonlinear dynamics present in forest fire data. Forest fires result from a complex interplay of factors, and the stepwise regression model can effectively select the most significant variables, providing an efficient analytical method for the multifaceted nature of forest fires. Given that fires are likely primarily influenced by climate change, the stepwise regression model can effectively identify the variables that pose the greatest risk to fire occurrence.  
This article reviews various studies on forest fire prediction models, analyzing the strengths and limitations of current methodologies. Building on existing research, we have identified a gap in the in-depth analysis of fire behavior under specific climatic conditions. Our study aims to fill this gap, examining the efficacy and limitations of different models when addressing the same issue. This can facilitate model optimization and provide a more comprehensive and diversified perspective for forest fire prediction through these two models.