For financial time series, the generation of error bars on the point prediction is important in order to estimate the corresponding risk. In recent years, artificial intelligence optimisation techniques have been used to make time series approaches more systematic and improve forecasting performance. The harmony search learning methodology, already successfully applied for training of multilayer perceptrons, is applied to Functional Link Artificial Neural Network (FLANN) in order to infer non-linear models for predicting a time series and the related volatility. The proposed method is implemented and the results are compared with FLANN model trained by back propagation and differential evolution. The proposed training method shows that FLANN-harmony search provides better forecasting/prediction as compared to training the FLANN model using back propagation or differential evolution.