Journal
INFORMATION SCIENCES
Volume 295, Issue -, Pages 107-125Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2014.09.002
Keywords
Local linear neuro-fuzzy model; Singular spectrum analysis; Particle swarm optimization; Time series forecasting
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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. (C) 2014 Published by Elsevier Inc.
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