<p>This study introduces a new hybrid fuzzy time series (FTS) model designed to improve forecasting abilities by independently applying forecasting techniques using both overlapping and non-overlapping partitions. By leveraging fuzzy logical relationships, the model enhances forecast reliability for decision-making. The independent application of the overlapping and non-overlapping partitions allows for a comparative analysis, demonstrating the superior forecasting accuracy of overlapping partitions. Evaluation using the Root Mean Square Error metric consistently demonstrates that the hybrid FTS model yields better forecasting results when overlapping partitions are considered. Real-world datasets across various domains, such as enrollment data from the University of Alabama, market indices, car sales figures, gold prices, lynx populations, sunspot counts, and car accidents, are employed to assess the effectiveness of the proposed FTS forecasting model. This evaluation highlights the potential of the hybrid FTS model as a dependable forecasting technique with broad applicability.</p>