A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning (CBR) system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a CBR model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the CBR system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. The results obtained from experiments, in which the system operated in a real environment, are presented.