Usually, real-world time-series forecasting problems are dynamic. If such time-series are characterized by mere concept shifts, a passive approach to learning become ideal to continuously adapt the model parameters whenever new data patterns arrive to cope with uncertainty in the presence of change. This work hybridizes a quantum-inspired particle swarm optimization designed for dynamic environments, to cope with concept shifts, with either a least-squares approximation technique or nonlinear autoregressive model to forecast time-series. Also, this work evaluates experimentally and performs a comparative study on the performance of the proposed models. The obtained results show that the nonlinear autoregressive-based model outperformed the least-squares approximation-based model and the separate models that were implemented in the hybridization and also, several state-of-the-art models for the given datasets.