Abstract
Stochastic models can be used for predicting the availability of residual woody forest biomass, considering the variability and uncertainty associated with climatic, operational, and economic factors. These models, such as ARIMA, GARCH, state transition models, and Monte Carlo simulations, are widely used to capture seasonal patterns, dynamic variations, and complex uncertainties. Their application supports critical decisions in forest and energy operations planning. The implementation of the models was carried out in Python, using specialized packages such as Statsmodels for ARIMA, Arch for GARCH, and PyMC3 for state transition models. Probabilistic calculations were performed with Numpy and Scipy, while Matplotlib and Seaborn were used for data visualization. Hypothetical data simulating real-world scenarios were analyzed, divided into training and testing sets, with cross-validation and metrics such as RMSE, MAPE, and R2. ARIMA demonstrated high accuracy in capturing seasonality, while GARCH effectively modeled volatility. Monte Carlo simulations provided the most reliable forecasts, capturing uncertainties across multiple scenarios. The models excelled in predicting periods of high biomass availability with robust projections. The results confirm the efficacy of stochastic models in predicting residual biomass, with a positive impact on sustainable planning. However, challenges such as data dependency and computational resources still need to be addressed, pointing to directions for future research and methodological improvements.