Self-Organized Hierarchical Methods for Time Series Forecasting

Abstract

Time series forecasting with the use of Artificial Neural Networks (ANN), in special with self-organized maps (SOM), has been explored in the literature with good results. One good strategy for improving computational cost and specialization of SOMs in general is constructing it via hierarchical structures. This work presents four different heuristics for constructing hierarchical SOMs for time series prediction, evaluating their computational cost and forecast precision and providing insight on future enhancements.