Unstable multivariate monitoring processes often exhibit stochastic chaos, where strong nonlinearity and timevarying dependencies limit the effectiveness of classical regression. This paper proposes a regression-based forecasting framework that exploits slowly varying cross-correlations between a target variable and selected immersion-environment parameters. Correlated regressors are compressed into a scalar integrated indicator and used in a rolling, shifted-design model. Three integration strategies are considered and combined within a twolevel multi-expert architecture via weighted averaging. Experiments on data from an unstable gas-dynamic technological process show that multi-expert aggregation reduces systematic forecast delay and improves accuracy, yielding an average MSE reduction of approximately 11% relative to the best single expert.