This paper introduces a new type of regime-switching model that uses nonlinear kernel-based representations to automatically detect and forecast shifts in financial market behavior across multiple time series. Its key innovation lies in uncovering hidden, time-varying patterns without prior knowledge of regimes—making it highly relevant for financial applications like volatility forecasting, risk assessment, and market trend analysis.