The Brazilian energy sector has undergone significant transformations, highlighting the crucial role of natural gas in ensuring energy security in the face of the transition to sources less dependent on fossil fuels. Forecasting natural gas demand is essential for efficient management of the sector. While the literature has focused on forecasting electricity demand, there is a gap in studies on modeling and forecasting natural gas demand, especially in industrial and medium/long-term contexts. The need for more accurate and comprehensive models to forecast natural gas demand is evident from the analysis of existing studies. Therefore, the objective of this work is to address a comparative analysis of natural gas demand forecasting, using models suggested in recent time series literature, with application in the R software, for daily and weekly natural gas consumption data, obtained of the Natural Gas Movement Reports in Transport Gas Pipelines, released monthly by the National Agency for Petroleum, Natural Gas and Biofuels, in the period from 2021 to 2023. The models provide forecasts for a test sample of thirty days in the future for daily and four weeks for weekly data and an out-of-sample comparative analysis is performed based on performance metrics to identify the most suitable model for the data series. At the end of the study, the forecast models using neural networks and tbats (Box-Cox transformation, ARMA errors, trend and trigonometric seasonal components) were those that demonstrated the best performance for daily data, while the decomposition method with autoregressive modeling and seasonal adjustment (stlar) and the seasonal naive method were the ones which showed better performance for time series on a weekly basis.