A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting

This paper develops a hybrid method for nonlinear and chaotic time series forecasting based on a local linear neuro-fuzzy model (LLNF) and optimized singular spectrum analysis (OSSA), termed OSSA–LLNF. Nonlinear and chaotic time series often exhibit complex behaviour and dynamics, turning their forecasting (particularly in multi-step ahead horizons) into a difficult task. In this paper, SSA is utilized for data processing, resulting in the elimination of noisy components and improvement of forecasting performance. The SSA parameters are fine-tuned using the particle swarm optimization algorithm. Then, the processed time series is modelled and forecasted via the LLNF model. The proposed OSSA–LLNF model is applied to forecast several well-known time series with different structures and characteristics. The comparison of the obtained results with those of several old and recently developed methods indicates the superiority and promising performance of the proposed OSSA–LLNF approach.