A hybrid fuzzy–ensemble method for time series forecasting

This study proposes a rule-free hybrid forecasting framework that integrates type-1 fuzzy function modeling with ensemble regression via random forests. The proposed hybrid fuzzy-ensemble model first constructs lagged representations of a univariate time series and applies fuzzy c-means clustering to obtain soft partitions of the input space. For each cluster, a dedicated random forest regressor is trained, and final forecasts are produced through membership-weighted aggregation of cluster-level predictions. This architecture eliminates the need for expert-defined fuzzy rules while enabling the model to capture both local (regime-dependent) and global temporal dynamics. Key hyperparameters, including lag order, number of clusters, membership thresholds, and random forest settings, are jointly optimized using random search on the training data. The framework is evaluated on thirteen real-world time series from financial, industrial, and environmental domains. Experimental results demonstrate competitive and, in several cases, improved forecasting accuracy compared to classical statistical methods, fuzzy regression models, and plain random forest baselines. The findings suggest that combining soft fuzzy partitioning with cluster-specific ensemble learning provides a flexible and data-driven mechanism for modeling nonlinear structures and gradual regime-like behaviors within univariate time series.