Forecasting models for prediction in time series

被引：6

作者：

Carpinteiro, Otavio A. S. ^{[1
]}

Leite, Joao P. R. R. ^{[1
]}

Pinheiro, Carlos A. M. ^{[1
]}

Lima, Isaias ^{[1
]}

机构：

[1] Univ Fed Itajuba, Res Grp Syst & Comp Engn, BR-37500903 Itajuba, MG, Brazil

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2012年 / 38卷 / 02期

关键词：

Kernel-based models; Neural models; Hierarchical models; Artificial intelligence; Financial time-series forecasting; SPATIOTEMPORAL CONNECTIONIST NETWORKS; TAXONOMY;

D O I：

10.1007/s10462-011-9275-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：163 / 171

页数：9

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