Muneef Abdulkareem Farea Ahmed

NFGA-LINEAR: An Explainable Neuro-Fuzzy Genetic Framework for Forecasting Anomaly-Affected Data-Scarce Time Series

Reliable forecasting becomes particularly challenging when a time series is short, nonlinear, and contaminated by occasional, abnormal observations. We develop NFGA-LINEAR, an interpretable Takagi–Sugeno–Kang neurofuzzy framework in which a genetic algorithm jointly determines the number of rules and optimizes Gaussian...