Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Neurocomputing
Time-series prediction using self-organising mixture autoregressive network
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Requirements for the learning of multiple dynamics
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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The self-organizing mixture autoregressive (SOMAR) model regards a time series as a mixture of regressive processes. A self-organizing algorithm is used with the LMS algorithm to learn the parameters of these regressive models. The self-organizing map is used to simplify the mixture as a winner-take-all selection of local models, combined with an autocorrelation coefficient based measure as the similarity measure for identifying correct local models. The SOMAR has been shown previously being able to uncover underlying autoregressive processes from a mixture. This paper proposes a generalized SOMAR that fully considers the mixing mechanism and individual model variances that make modeling and prediction more accurate for non-stationary time series. Experiments on both benchmark and financial time series are presented. The results demonstrate the superiority of the proposed method over other time-series modeling techniques on a range of performance measures.