This paper proposes a novel nonparametric support vector machine (SVM)-based approach for modeling and predicting fuzzy time-dependent data. Unlike traditional parametric methods such as ARIMA models and linear regression, which rely on rigid assumptions, the proposed framework leverages the flexibility of SVMs to capture complex, nonlinear temporal patterns in observations represented as fuzzy numbers. The nonparametric approach is chosen for its flexibility, robustness to model misspecification, and ability to accommodate irregular patterns and outliers common in fuzzy time series data. A practical example from software development quality monitoring motivates the need for this approach, where conventional SVM methods cannot directly handle fuzzy observations or uncertainty captured by spreads. To evaluate performance, the model is applied to both simulated fuzzy time series and a real-world dataset. A comprehensive set of evaluation criteria is employed: a similarity-based measure (MSM) for overall fuzzy similarity, root mean squared error (RMSE) for center accuracy, and average spread error (ASE) for spread accuracy. Empirical results demonstrate that the proposed method consistently outperforms existing methods across all three metrics. Furthermore, a comparison with a linear SVM model confirms the necessity of the nonlinear kernel for capturing complex temporal patterns, while the BDS test confirms the presence of nonlinear structure in the real dataset. The findings also indicate that the fitted models are robust and remain stable even in the presence of outliers. Overall, the results suggest that this SVM-based nonparametric framework offers a powerful, accurate, and robust tool for modeling fuzzy time-dependent data in various practical applications.