Time series forecasting is a fundamental challenge in many scientific and industrial domains, especially when dealing with chaotic and highly nonlinear systems. Traditional forecasting models, including Artificial Neural Networks (ANNs) and statistical methods, often struggle to capture the complex temporal dependencies and uncertainty inherent in these datasets. To address these limitations, hybrid approaches that integrate fuzzy logic and Recurrent Neural Networks have emerged as promising alternatives. This work presents a comparative study of two hybrid models: the Multi-Functional Recurrent Fuzzy Neural Network and the Recurrent Neurofuzzy System ReNFuzz-LF. The effectiveness of these models is evaluated by a series of experiments based on datasets of different sources, sizes and characteristics, including wind speed, the Google Stock Price Prediction and the Air Quality Index.