Forecasting models for prediction in time series

摘要

This paper presents the study of three forecasting models—a multilayer perceptron, a support vector machine, and a hierarchical model. The hierarchical model is made up of a self-organizing map and a support vector machine—the latter on top of the former. The models are trained and assessed on a time series of a Brazilian stock market fund. The results from the experiments show that the performance of the hierarchical model is better than that of the support vector machine, and much better than that of the multilayer perceptron.