Akademik Veri Yönetim Sistemi
International journal of advances in engineering and pure sciences (Online), cilt.38, sa.1, ss.199-209, 2026 (TRDizin)
This study introduces a forecasting framework for financial time series that combines multiple forecaster functions built on Picture Fuzzy C-Means (PFCM) clustering. In the proposed framework, the time series is embedded into a lagged-variable space and clustered using Picture Fuzzy C-Means (PFCM), which assigns to each time point three degrees: positive (𝜇), neutral (𝜂), and negative (𝜈). For each degree and each cluster, a separate multiple linear regression forecaster is constructed using the corresponding degree, selected nonlinear transformations of that degree, and lagged variables as inputs, while sharing the same target values. Consequently, the procedure produces 3 × 𝐶 base forecasts that are aggregated in two stages: base forecasts are first combined using the associated degree information and then refined through the neutral/indeterminacy structure to obtain the final forecast. By representing uncertainty through three complementary degrees and enriching the input space with degree-based nonlinear features, the framework captures both linear and nonlinear patterns in a transparent manner. The resulting Picture Fuzzy C-Means–based ensemble of forecasting functions is empirically evaluated on several widely used financial time-series benchmarks and demonstrates competitive forecasting performance.